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Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning 认领 引用
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作者 Saman Riaz Ali Arshad +1 位作者 Shahab S.Band Amir Mosavi 《Computers, Materials & Continua》 SCIE EI 2022年第9期4303-4321,共19页
The way towards generating a website front end involves a designersettling on an idea for what kind of layout they want the website to have, thenproceeding to plan and implement each aspect one by one until they havec... The way towards generating a website front end involves a designersettling on an idea for what kind of layout they want the website to have, thenproceeding to plan and implement each aspect one by one until they haveconverted what they initially laid out into its Html front end form, this processcan take a considerable time, especially considering the first draft of the designis traditionally never the final one. This process can take up a large amountof resource real estate, and as we have laid out in this paper, by using a Modelconsisting of various Neural Networks trained on a custom dataset. It can beautomated into assisting designers, allowing them to focus on the other morecomplicated parts of the system they are designing by quickly generating whatwould rather be straightforward busywork. Over the past 20 years, the boomin how much the internet is used and the sheer volume of pages on it demands ahigh level of work and time to create them. For the efficiency of the process, weproposed a multi-model-based architecture on image captioning, consisting ofConvolutional neural network (CNN) and Long short-term memory (LSTM)models. Our proposed approach trained on our custom-made database can beautomated into assisting designers, allowing them to focus on the other morecomplicated part of the system. We trained our model in several batches overa custom-made dataset consisting of over 6300 files and were finally able toachieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50hand-drawn images at 87.86%. 展开更多
关键词 Deep learning wireframes front-end low fidelity high fidelity design process html computer vision dsl
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Deep Bimodal Fusion Approach for Apparent Personality Analysis 认领 引用
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作者 Saman Riaz Ali Arshad +1 位作者 Shahab S.Band Amir Mosavi 《Computers, Materials & Continua》 SCIE EI 2023年第4期2301-2312,共12页
Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research ... Personality distinguishes individuals’ patterns of feeling, thinking,and behaving. Predicting personality from small video series is an excitingresearch area in computer vision. The majority of the existing research concludespreliminary results to get immense knowledge from visual and Audio(sound) modality. To overcome the deficiency, we proposed the Deep BimodalFusion (DBF) approach to predict five traits of personality-agreeableness,extraversion, openness, conscientiousness and neuroticism. In the proposedframework, regarding visual modality, the modified convolution neural networks(CNN), more specifically Descriptor Aggregator Model (DAN) areused to attain significant visual modality. The proposed model extracts audiorepresentations for greater efficiency to construct the long short-termmemory(LSTM) for the audio modality. Moreover, employing modality-based neuralnetworks allows this framework to independently determine the traits beforecombining them with weighted fusion to achieve a conclusive prediction of thegiven traits. The proposed approach attains the optimal mean accuracy score,which is 0.9183. It is achieved based on the average of five personality traitsand is thus better than previously proposed frameworks. 展开更多
关键词 Apparent personality analysis deep bimodal fusion convolutional neural network long short-term memory bimodal information fusion approach
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