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نتیجه جستجو - Thoughts

تعداد مقالات یافته شده: 26
ردیف عنوان نوع
1 الگوریتم ژنتیک چند هدفه و طرح معماری یادگیری عمیق مبتنی بر CNN برای تشخیص موثر spam
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 18
معمولا ایمیل به عنوان قدرتمندترین رسانه در شبکه‌های اجتماعی آنلاین در نظر گرفته می‌شود که امکان گفتگو و ارتباط آنلاین کاربران رسانه‌های اجتماعی آنلاین را با یکدیگر فراهم می کند، همچنین امکان اشتراک گذاری لینک هم وجود دارد. به ویژه، توییتر به عنوان محبوب ترین شبکه اجتماعی شناخته شده است که بهترین کانال ارتباطی برای به اشتراک گذاشتن اخبار، ایده ها، افکار، نظرات و عقاید فعلی کاربران خود با سایر کاربران رسانه های اجتماعی آنلاین است. علیرغم تلاش‌هایی که برای مبارزه با عملیات اسپم در شبکه اجتماعی آنلاین انجام شده است، اسپم توییتر دارای عملکرد جدیدی محدود به 140 کاراکتر است. این نه تنها علت اصلی آزار کاربران روزمره است، بلکه اکثر مسائل امنیتی رایانه نیز ناشی از آن است که میلیاردها دلار کاهش بهره وری هزینه را در پی دارد. در این مقاله، یک الگوریتم ژنتیک چندهدفه و یک طرح معماری یادگیری عمیق مبتنی بر CNN (MOGA-CNN-DLAS) برای فرآیند تشخیص اسپم غالب در توییتر پیشنهاد می‌کنیم. جزئیات تجربی و نتایج و بحث حاصل از MOGA-CNN-DLAS پیشنهادی از نظر دقت ، صحت، فراخوان، FScore، RMSE و MAE مورد ارزیابی قرار گرفتند. این ارزیابی با تغییر نسبت داده‌های آموزشی کاربردی از سه مجموعه داده واقعی، مانند مجموعه داده توییتر k100 و ASU انجام شد.
کلمات کلیدی: اسپم توییتر | یادگیری عمیق | شبکه عصبی پیچشی یا همگشتی (CNN) | الگوریتم ژنتیک | آنالیز رسانه های اجتماعی | تشخیص موثر اسپم
مقاله ترجمه شده
2 Machine learning: Best way to sustain the supply chain in the era of industry 4:0
یادگیری ماشین: بهترین راه برای حفظ زنجیره تأمین در عصر صنعت 4:0-2021
With the rapidly growing importance in the industries on the adaptation of advanced technologies, the involvement of IT-enabled systems has increased in developing the pathway for the future industry. The learning’s from these technologies becomes paramount for the present industries which gives a sense of belongingness and significance of the industry towards the market. The digital revolution world-wide affected the physical happenings of the events in the manufacturing industries such as the procurement, manufacturing/assembling & distribution of goods. This digital reformation is known as Industry 4.0 which generally means the advancement in the existing business models where all the business operations are interconnected with each other by digital mode (virtual representation based on operations). In this kind of environment, it is being necessary to map all the operations digitally in such a manner so that the physical flows of resources/goods will not suffer at any stage. Machine learning in the present scenario is one of the thrust areas for the researchers and the practitioners. The output in the machine learning process is having many dependencies on the input data such as the functions and characteristics imparted to the machine at the earlier stage. The present paper aptly reflects the thoughts and reflections of present-day industries and the opportunities to express feelings, thoughts, and contribute towards the future industries.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International Conference on Computational and Experimental Methods in Mechanical Engineering.
Keywords: Machine Learning (ML) | Supply Chain (SC) | Industry 4.0 | Resources utilization | Digital transformation
مقاله انگلیسی
3 Vision-based hand signal recognition in construction: A feasibility study
تشخیص سیگنال دست مبتنی بر چشم انداز در ساخت و ساز: یک مطالعه امکان سنجی-2021
In construction fields, it is common for workers to rely on hand signals to communicate and express thoughts due to their simple but effective nature. However, the meaning of these hand signals was not always captured precisely. As a result, construction errors and even accidents were produced. This paper presented a feasibility study on investigating whether the hand signals could be captured and interpreted automatically with computer vision technologies. It starts with the literature review of existing hand gesture recognition methods for sign language understanding, human-computer interaction, etc. It is then followed by creating a dataset containing 11 classes of hand signals in construction. The performance of two state-of-the-art 3D convolutional neural networks is measured and compared. The results indicated that a high classification accuracy (93.3%) and a short inference time (0.17 s/gesture) could be achieved, illustrating the feasibility of using computer vision to automate hand signal recognition in construction.
Keywords: Hand signal recognition | Dataset creation | Performance comparison | Feasibility study
مقاله انگلیسی
4 Measurement-based care in forensic psychiatry
مراقبت مبتنی بر اندازه گیری در روانپزشکی قانونی-2020
Measurement-based care (MBC) is the systematic evaluation of a patient’s symptoms or factors before or during an encounter. It is used to inform treatment and behavioral health interventions. This article argues that MBC is the natural consequence flowing from evidence-based practice. In this article, MBC is defined and explained IN detail. Barriers to the implementation of MBC are presented and methods of selecting a measurement tool are evaluated. The article describes areas where MBC can be applied in forensic settings, and specific risk assessment tools are presented and evaluated, including the HCR-20v3, DASA-IV, DUNDRUM, and CGI–C. The article emphasizes how imperative it is that physicians use MBC and discusses why forensic practice is ideally suited to MBC. Measurement-based care in forensic psychiatry often say that when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts, advanced to the stage of science, whatever the matter may be. (Lord Kelvin, 1889).
Keywords: Measurement-based care | Evidence-based practice | Forensic psychiatry | Risk assessment
مقاله انگلیسی
5 Associations of Lesbian, Gay, Bisexual, Transgender, and QuestioningeInclusive Sex Education With Mental Health Outcomes and School-Based Victimization in U:S: High School Students
انجمن های لزبین ، همجنسگرا ، دوجنسگرا ، ترنسجندر و سؤال از آموزش فراگیر جنسی با پیامدهای سلامت روانی و قربانی سازی مدارس در دانش آموزان دبیرستانی ایالات متحده-2019
Purpose: Homophobic school climates are related to increased victimization for sexual minority youth (SMY), leading to increased risk of adverse mental health outcomes. Interventions that promote positive school climate may reduce the risk of victimization and adverse mental health outcomes in SMY. This study explored whether lesbian, gay, bisexual, transgender, and questioning (LGBTQ)einclusive sex education is associated with adverse mental health and school-based victimization in U.S. youth. Methods: Data analysis of representative data from the 2015 Youth Risk Behavior Survey and the 2014 School Health Profiles was conducted using multilevel logistic models testing whether youth in states with higher proportions of schools teaching LGBTQ-inclusive sex education had lower odds of reporting being bullied in school and experiencing adverse mental health outcomes, including depressive symptoms and suicidality. Results: After controlling for covariates, protective effects for all youth were found for suicidal thoughts (adjusted odds ratio [AOR]: .91, 95% confidence interval [CI]: .89e.93) and making a suicide plan (AOR: .79; 95% CI: .77e.80). Lesbian and gay youth had lower odds of experiencing bullying in school as the proportion of schools within a state teaching LGBTQ-inclusive sex education increased (AOR: .83; CI: .71e.97). Bisexual youth had significantly lower odds of reporting depressive symptoms (AOR: .92; 95% CI: .87e.98). Conclusions: Students in states with a greater proportion of LGBTQ-inclusive sex education have lower odds of experiencing school-based victimization and adverse mental health. These findings can be used to guide intervention development at the school and state levels
Keywords: Sexual minority youth | Mental health | Bullying | Inclusive sex education | School climate
مقاله انگلیسی
6 Beyond public punitiveness: The role of emotions in criminal law policy
فراتر از مجازات عمومی: نقش عواطف در سیاست کیفری-2019
The article examines the existing and potential role of emotions in the criminal law-making and criminal policy. It aims to inspect which emotions, if any, are more acceptable for influencing criminal policy and to what extent emotions could legitimately intervene in criminalisation processes. It first analyses the ways in which emotion has already penetrated into the criminal law, criminal justice and criminalisation. Next, it inspects the various characteristics of emotions, specifically those that are central in distinguishing between good and bad candidates for influencing criminal law policy, demonstrating that certain negative, highly intense, irrational and unstable or short-lived emotions can make bad law, as do atypical cases. The article then sketches a theoretical framework, composed of the requirements that should be fulfilled before any emotion could justifiably influence criminal law-making and of the further limits to such an enterprise. It concludes with recommendations and some thoughts on further research.
Keywords: Emotion | Criminalisation | Criminal law policy | Public sentiment | Legitimacy | Justice
مقاله انگلیسی
7 Self-powered gait pattern-based identity recognition by a soft and stretchable triboelectric band
شناخت هویت مبتنی بر الگوی راه رفتن خود قدرت توسط یک باند برق نرم و کششی-2019
Since each individual has distinct gait characteristics, monitoring human motion can enable identity recognition. Here, we report a self-powered band that can recognize human identity through gait pattern which is achieved by detecting muscle activity. The self-powered band is a soft and stretchable triboelectric nanogenerator (TENG) that is biocompatible and low-cost, which is looped around human body parts and generates electrical outputs in response to body motions involving muscle activities. The band can quantitatively detect walking step, speed and distance. Furthermore, the detected unique motion pattern of each individual allows the band to be used for identity recognition such as personal computer login and employee clock in through gait monitoring and analysis. This work opens new frontiers for the development of self-powered electronics and inspires new thoughts in human-machine interface.
Keywords: Triboelectric nanogenerator | Self-powered sensor | Human motion detection | Gait pattern | Identity recogni
مقاله انگلیسی
8 The use of machine learning in the study of suicidal and non-suicidal selfinjurious thoughts and behaviors: A systematic review
استفاده از یادگیری ماشینی در مطالعه افکار و رفتارهای خودکشی و اقدام به خودکشی: یک بررسی سیستماتیک-2019
Background: Machine learning techniques offer promise to improve suicide risk prediction. In the current systematic review, we aimed to review the existing literature on the application of machine learning techniques to predict self-injurious thoughts and behaviors (SITBs). Method: We systematically searched PsycINFO, PsycARTICLES, ERIC, CINAHL, and MEDLINE for articles published through February 2018. Results: Thirty-five articles met criteria to be included in the review. Included articles were reviewed by outcome: suicide death, suicide attempt, suicide plan, suicidal ideation, suicide risk, and non-suicidal self-injury. We observed three general aims in the use of SITB-focused machine learning analyses: (1) improving prediction accuracy, (2) identifying important model indicators (i.e., variable selection) and indicator interactions, and (3) modeling underlying subgroups. For studies with the aim of boosting predictive accuracy, we observed greater prediction accuracy of SITBs than in previous studies using traditional statistical methods. Studies using machine learning for variable selection purposes have both replicated findings of well-known SITB risk factors and identified novel variables that may augment model performance. Finally, some of these studies have allowed for subgroup identification, which in turn has helped to inform clinical cutoffs. Limitations: Limitations of the current review include relatively low paper sample size, inconsistent reporting procedures resulting in an inability to compare model accuracy across studies, and lack of model validation on external samples. Conclusions: We concluded that leveraging machine learning techniques to further predictive accuracy and identify novel indicators will aid in the prediction and prevention of suicide.
Keywords: Machine learning | Suicide | Suicide attempt | Suicide risk | Suicidal ideation | Non-suicidal self-injury | Big data | Pattern recognition | Exploratory data mining
مقاله انگلیسی
9 Applications of Big Social Media Data analysis: An Overview
برنامه های تحلیل داده های اجتماعی رسانه هابزرگ : یک مرور کلی-2018
Over the last few years, online communication has moved toward user-driven technologies, such as online social networks (OSNs), blogs, online virtual communities, and online sharing platforms. These social technologies have ushered in a revolution in user-generated data, online global communities, and rich human behavior-related content. Human-generated data and human mobility patterns have become important steps toward developing smart applications in many areas. Understanding human preferences is important to the development of smart applications and services to enable such applications to understand the thoughts and emotions of humans, and then act smartly based on learning from social media data. This paper discusses the role of social media data in comprehending online human data and in consequently different real applications of SM data for smart services are executed.
Keywords: Online social networks (OSN), social media (SM), big social data, machine learning, smart society
مقاله انگلیسی
10 Stay alert: Forecasting the risks of sexting in Korea using social big data
هشدار: پیش بینی خطرات جنسیت در کره با استفاده از داده های بزرگ اجتماعی-2018
Youth sexting, which is commonly defined as the intimate image sharing of persons under 18, is an emerging phenomenon that has garnered significant attention in South Korea and in particular, the South Korean government. Widely recognized for its potential to generate undue harm, the South Korean government has initiated a movement determined to block the participation of obscene content sharing between youths under the age of 18. While there may be different avenues to examine this phenomenon from, an approach notably absent from this list is the use of big data and data mining information produced via the dispersion of the Internet and social media. Using social big data, the study found that teenagers sexting in hopes of obtaining a higher volume of attention among friends; file sharing is more frequented than image distribution through sexting; and transactions without “adult pornography” and with “smishing” were the most influential in addressing the risks of sexting in South Korea. While big data and data mining do not make any inferences themselves, the benefits of analyzing social big data lies in its ability to incorporate a much larger volume of data and confirm the thoughts of a diverse range of participants.
Keywords: Social big data ، Data mining ، Youth sexting ، South Korea ، Trends and patterns
مقاله انگلیسی
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