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نتیجه جستجو - Hybrid approach

تعداد مقالات یافته شده: 26
ردیف عنوان نوع
1 Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study
رانندگان ، موانع و ملاحظات اجتماعی برای پذیرش هوش مصنوعی در مشاغل و مدیریت: یک مطالعه عالی-2020
The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews. The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise their findings related to enablers, barriers and social implications of the AI adoption in business and management. The methodology used for this tertiary study is based on Kitchenham and Charter’s guidelines [14], resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2,021 primary studies. These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain, health and safety, project management, decisionsupport, systems management and technology acceptance). While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders’ perspectives. Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors and implications of the AI adoption. Most of the selected reviews are recommending an increased focus on social aspects of AI, in addition to more rigorous evaluation, use of hybrid approaches (AI and non-AI) and multidisciplinary approaches to AI design and evaluation. Furthermore, this study found that there is a lack of systematic reviews in some of the AI early adopter sectors such as financial industry and retail and that the existing systematic reviews are not focusing enough on human, organisational or societal implications of the AI adoption in their research objectives.
Keywords: artificial intelligence | business | machine learning | management | systematic literature review | tertiary study
مقاله انگلیسی
2 A survey of hybrid metaheuristics for the resource-constrained project scheduling problem
بررسی استعاره ترکیبی برای مشکل برنامه ریزی پروژه با محدودیت منابع-2020
The Resource-Constrained Project Scheduling Problem (RCPSP) is a general problem in scheduling that has a wide variety of applications in manufacturing, production planning, project management, and var- ious other areas. The RCPSP has been studied since the 1960s and is an NP-hard problem. As being an NP-hard problem, solution methods are primarily heuristics. Over the last two decades, the increasing interest in operations research for metaheuristics has resulted in a general tendency of moving from pure metaheuristic methods for solving the RCPSP to hybrid methods that rely on different metaheuristic strategies. The purpose of this paper is to survey these hybrid approaches. For the primary hybrid meta- heuristics that have been proposed to solve the RCPSP over the last two decades, a description of the basic principles of the hybrid metaheuristics is given, followed by a comparison of the results of the dif- ferent hybrids on the well-known PSPLIB data instances. The distinguishing features of the best hybrids are also discussed.
Keywords: Project scheduling| Resource constraints | RCPSP | Metaheuristics | Hybrids
مقاله انگلیسی
3 مروری بر تجمیع دستگاه های مدل سازی اطلاعات ساختمانی (BIM) و اینترنت اشیاء (IoT): وضعیت کنونی و روند آینده
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 56
تجمیع مدل سازی اطلاعات ساختمانی (BIM) با داده های زمان واقعی(بلادرنگ) دستگاه های اینترنت اشیاء (IoT)، نمونه قوی را برای بهبود ساخت وساز و بهره وری عملیاتی ارائه می دهد. اتصال جریان-های داده های زمان واقعی که بر گرفته از مجموعه هایی از شبکه های حسگرِ اینترنت اشیاء (که این جریان های داده ای، به سرعت در حال گسترش هستند) می باشند، با مدل های باکیفیت BIM، در کاربردهای متعددی قابل استفاده می باشد. با این حال، پژوهش در زمینه ی تجمیع BIM و IOT هنوز در مراحل اولیه ی خود قرار دارد و نیاز است تا وضعیت فعلی تجمیع دستگاه های BIM و IoT درک شود. این مقاله با هدف شناسایی زمینه های کاربردی نوظهور و شناسایی الگوهای طراحی رایج در رویکردی که مخالف با تجمیع دستگاه BIM-IoT می باشد، مرور جامعی در این زمینه انجام می دهد و به بررسی محدودیت های حاضر و پیش بینی مسیرهای تحقیقاتی آینده می پردازد. در این مقاله، در مجموع، 97 مقاله از 14 مجله مربوط به AEC و پایگاه داده های موجود در صنایع دیگر (در دهه گذشته)، مورد بررسی قرار گرفتند. چندین حوزه ی رایج در این زمینه تحت عناوین عملیات ساخت-وساز و نظارت، مدیریت ایمنی و بهداشت، لجستیک و مدیریت ساختمان، و مدیریت تسهیلات شناسایی شدند. نویسندگان، 5 روش تجمیع را همراه با ذکر توضیحات، نمونه ها و بحث های مربوط به آنها به طور خلاصه بیان کرده اند. این روش های تجمیع از ابزارهایی همچون واسط های برنامه نویسی BIM، پایگاه داده های رابطه ای، تبدیل داده های BIM به پایگاه داده های رابطه ای با استفاده از طرح داده های جدید، ایجاد زبان پرس وجوی جدید، فناوری های وب معنایی و رویکردهای ترکیبی، استفاده می کنند. براساس محدودیت های مشاهده شده، با تمرکز بر الگوهای معماری سرویس گرا (SOA) و راهبردهای مبتنی بر وب برای ادغام BIM و IoT، ایجاد استانداردهایی برای تجمیع و مدیریت اطلاعات، حل مسئله همکاری و محاسبات ابری، مسیرهای برجسته ای برای تحقیقات آینده پیشنهاد شده است.
کلمه های کلیدی: مدل سازی اطلاعات ساختمانی (BIM) | دستگاه اینترنت اشیاء (IoT) | حسگرها | ساختمان هوشمند | شهر هوشمند | محیط ساخته شده هوشمند | تجمیع.
مقاله ترجمه شده
4 A hybrid approach using machine learning and genetic algorithm to inverse modeling for single sphere scattering in a Gaussian light sheet
یک روش ترکیبی با استفاده از یادگیری ماشین و الگوریتم ژنتیک برای مدل سازی معکوس برای پراکندگی تک کره در یک صفحه نور گاوسی-2019
Light scattering has been proven to be an effective tool to characterize and classify particles of different properties. However, inverse modeling to quantitatively retrieve the particle property from light scattering is still a tough task in most applications. In this paper, a hybrid approach using machine learning and genetic algorithm is developed to obtain the geometrical and optical parameters of a sphere from its angular scattering pattern in a light sheet. Scattering patterns related to different parameters are first generated by numerically solving Mie scattering based on angular spectrum theory. Multilayer perception neural network (NN) is then employed to roughly estimate the parameter, while genetic algorithm is adopted to retrieve the precise value. Influences of intensity noise on the inverse modeling are finally examined. Results suggest that the proposed hybrid approach can retrieve the parameters of the sphere from its scattering pattern with high precision in a time-effective manner, which could be widely applied in various scattering-based instruments
Keywords: Mie scattering | Inverse modeling | Machine learning | Genetic algorithm | Gaussian light sheet
مقاله انگلیسی
5 A hybrid deep learning image-based analysis for effective malware detection
یک تجزیه و تحلیل مبتنی بر یادگیری عمیق ترکیبی برای تشخیص مؤثر بدافزار -2019
The explosive growth of Internet and the recent increasing trends in automation using intelligent appli- cations have provided a veritable playground for malicious software (malware) attackers. With a variety of devices connected seamlessly via the Internet and large amounts of data collected, the escalating mal- ware attacks and security risks are a big concern. While a number of malware detection methods are available, new methods are required to match with the scale and complexity of such a data-intensive environment. We propose a novel and unified hybrid deep learning and visualization approach for an effective detection of malware. The aim of the paper is two-fold: 1. to present the use of image-based techniques for detecting suspicious behavior of systems, and 2. to propose and investigate the application of hybrid image-based approaches with deep learning architectures for an effective malware classification. The performance is measured by employing various similarity measures of malware behavior patterns as well as cost-sensitive deep learning architectures. The scalability is benchmarked by testing our proposed hybrid approach with both public and privately collected large malware datasets that show high accuracy of our malware classifiers.
Keywords: Malware detection | Similarity mining | Image analysis | Evaluation metrics | Machine learning | Deep learning architectures
مقاله انگلیسی
6 A hybrid approach to building face shape classifier for hairstyle recommender system
یک روش ترکیبی برای ساخت طبقه بندی فرم صورت برای سیستم توصیه کننده مدل مو-2019
Identifying human face shape is the first and the most vital process prior to choosing the right hairstyle to wear on according to guidelines from hairstyle experts, especially for women. This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This frame- work enables an automatic hairstyle recommendation with a single face image. This has a direct impact on beauty industry service providers. It can simulate how the user looks like when she is wearing the chosen hairstyle recommended by the expert system. The model used in this framework is based on Sup- port Vector Machine. The framework is evaluated on hand-crafted, deep-learned (VGG-face) features and VGG-face fine-tuned version for the face shape classification task. In addition to evaluating these indi- vidual features by a well-designed framework, we attempted to fuse these three descriptors together in order to improve the performance of the classification task. Two combination techniques were employed, namely: Vector Concatenation and Multiple Kernel Learning (MKL) techniques. All the hyper-parameters of the model were optimised by using Particle Swarm Optimisation. The results show that combining hand-crafted and VGG-face descriptors with MKL yielded the best results at 70.3% of accuracy which was statistically significantly better than using individual features. Thus, combining multiple representations of the data with MKL can improve the overall performance of the expert system. In addition, this proves that hand-crafted descriptor can be complementary to deep-learned descriptor.
Keywords: Face shape classification | Deep-learned feature | Hand-crafted feature | Hybrid feature-based approach | Feature combination
مقاله انگلیسی
7 Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution
قطعه بندی تصویر اولتراسوند با استفاده از یک خوشه بندی فازی هسته ای گاوسی چند مقیاسی و پیچیدگی میدان بردار چند مقیاسی-2019
Ultrasound imaging is most popular technique used for breast cancer screening. Lesion segmentation is challenging step in characterization of breast ultrasound (US) based Computer Aided Diagnosis (CAD) systems due to presence of speckle noise, shadowing effect etc. The aim of this study is to develop an automatic lesion segmentation technique in breast US with high accuracy even in presence of noises, artifacts and multiple lesions. This article presents a novel clustering method called Multi-scale Gaussian Kernel induced Fuzzy C -means (MsGKFCM) for segmentation of lesions in automatically extracted Region of Interest (ROI) in US to delimit the border of the mass. Further, a hybrid approach using MsGKFCM and Multi-scale Vector Field Convolution (MsVFC) is proposed to obtain an accurate lesion margin in breast US images. Initially, the images are filtered using speckle reducing anisotropic diffusion (SRAD) technique. Subsequently, MsGKFCM is applied on filtered images to segment the mass and detect an appropriate cluster center. The detected cluster center is further used by MsVFC to determine the accurate lesion margin. The proposed technique is evaluated on 127 US images using measures such as Jaccard Index, Dice similarity, Shape similarity, Hausdroffdifference, Area difference, Accuracy, F -measure and analysis of variance (ANOVA) test. The empirical results suggest that the proposed approach can be used as an expert system to assist medical professionals by providing objective evidences in breast lesion detection. Results obtained are so far looking promising and effective in comparison to state-of-the-art algorithms.
Keywords: Ultrasound image segmentation | Speckle reduction | Multi-scale Gaussian kernel induced fuzzy | C -means | Multi-scale vector field convolution
مقاله انگلیسی
8 A hybrid route planning approach for logistics with pickup and delivery
یک رویکرد برنامه ریزی مسیر ترکیبی برای تدارکات با پیکاپ و تحویل-2019
With the busy life of modern people, more and more consumers are preferring to shop online. This change on shopping behavior results in large volumes of packages must be transported, and thus re- search on logistics planning considering real constraints has increased. To solve this problem, several heuristics or evolutionary methods with expert knowledge were proposed previously, but they are usu- ally inefficient or need a large amount of memory. In this paper, we propose a hybrid approach called Iterative Logistics Solution Planner ( ILSP ) for not only quickly finding a nice logistics solution but also itera- tively improving the solution quality while meeting the real logistics constraints. ILSP contains two main phases including initial logistics solution generation and iterative logistics solution improvement based on the intelligence and knowledge from domain experts. Several algorithms and strategies are designed in ILSP for package partitioning, route planning and quality improvement. From the view of expert sys- tems, the significance and impact of ILSP are simultaneously taking both computational efficiency and iterative quality improvement based on the expert knowledge into account on logistics planning problem with pickup and delivery. Through the rigorous experimental evaluations of real logistics data, the results demonstrated the excellent performance of ILSP .
Keywords: Hybrid approach | Logistics planning | Smart city| Expert system
مقاله انگلیسی
9 A hybrid approach to building face shape classifier for hairstyle recommender system
یک روش ترکیبی برای ساخت طبقه بندی فرم صورت برای سیستم توصیه کننده مدل مو-2019
Identifying human face shape is the first and the most vital process prior to choosing the right hairstyle to wear on according to guidelines from hairstyle experts, especially for women. This work presents a novel framework for a hairstyle recommender system that is based on face shape classifier. This frame- work enables an automatic hairstyle recommendation with a single face image. This has a direct impact on beauty industry service providers. It can simulate how the user looks like when she is wearing the chosen hairstyle recommended by the expert system. The model used in this framework is based on Sup- port Vector Machine. The framework is evaluated on hand-crafted, deep-learned (VGG-face) features and VGG-face fine-tuned version for the face shape classification task. In addition to evaluating these indi- vidual features by a well-designed framework, we attempted to fuse these three descriptors together in order to improve the performance of the classification task. Two combination techniques were employed, namely: Vector Concatenation and Multiple Kernel Learning (MKL) techniques. All the hyper-parameters of the model were optimised by using Particle Swarm Optimisation. The results show that combining hand-crafted and VGG-face descriptors with MKL yielded the best results at 70.3% of accuracy which was statistically significantly better than using individual features. Thus, combining multiple representations of the data with MKL can improve the overall performance of the expert system. In addition, this proves that hand-crafted descriptor can be complementary to deep-learned descriptor.
Keywords: Face shape classification | Deep-learned feature | Hand-crafted feature | Hybrid feature-based approach | Feature combination
مقاله انگلیسی
10 Towards better efficiency of interatomic linear machine learning potentials
به سمت بهره وری بهتر پتانسیل های یادگیری ماشین خطی بین قومی-2019
Interatomic machine learning potentials have achieved maturity and became worthwhile alternative to conventional interatomic potentials. In this work we profile some characteristics of linear machine learning methods. Being numerically fast and easy to implement, these methods offer many advantages and appear to be very attractive for large length and time scale calculations. However, we emphasize that in order to be accurate on some target properties these methods eventually yield overfitting. This feature is rather independent of training database and descriptor accuracy. At the same time, the major weakness of these potentials, i.e., lower accuracy with respect to the kernel potentials, proves to be their strength: within the confidence limits of the potential fitting, one can rely on less accurate but faster descriptors in order to boost the numerical efficiency. Here, we propose a hybrid type of atomic descriptor that combines the original forms of radial and spectral descriptors. Flexibility in choice of mixing proportions between the two descriptors ensures a user defined control over accuracy/numerical efficiency of the resulting hybrid descriptor form. The performance and features of the above linear machine learning potentials are investigated for the interatomic interactions in metals of primary importance for fusion and fission applications, Fe and W. The suggested hybrid approach opens many avenues in the field of linear machine learning potentials that up to now are preferentially coupled with more robust and computationally expensive spectral descriptors.
Keywords: Interatomic potentials | Machine learning | Descriptors | SNAP | Molecular dynamics
مقاله انگلیسی
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