Many leaf diseases that affect crop health cause severe mango farming concerns. This study employed deep learning techniques to analyze mango leaf disease categorization comprehensively. This study looks at the classi...
详细信息
Unmanned aerial vehicles (UAVs) have garnered increasing attention in recent years due to their utilization of artificial intelligence (AI) technologies and automation processes. These vehicles are being developed for...
详细信息
The popularity of multicore processors and the rise of High Performance computing as a Service (HPCaaS) have made parallel programming essential to fully utilize the performance of multicore systems. OpenMP, a widely ...
详细信息
ISBN:
(纸本)9798350386783;9798350386776
The popularity of multicore processors and the rise of High Performance computing as a Service (HPCaaS) have made parallel programming essential to fully utilize the performance of multicore systems. OpenMP, a widely adopted shared-memory parallel programming model, is favored for its ease of use. However, it is still challenging to assist and accelerate automation of its parallelization. Although existing automation tools such as Cetus and DiscoPoP to simplify the parallelization, there are still limitations when dealing with complex data dependencies and control flows. Inspired by the success of deep learning in the field of Natural Language Processing (NLP), this study adopts a Transformer-based model to tackle the problems of automatic parallelization of OpenMP instructions. We propose a novel Transformer-based multimodal model, ParaMP, to improve the accuracy of OpenMP instruction classification. The ParaMP model not only takes into account the sequential features of the code text, but also incorporates the code structural features and enriches the input features of the model by representing the Abstract Syntax Trees (ASTs) corresponding to the codes in the form of binary trees. In addition, we built a BTCode dataset, which contains a large number of C/C++ code snippets and their corresponding simplified AST representations, to provide a basis for model training. Experimental evaluation shows that our model outperforms other existing automated tools and models in key performance metrics such as F1 score and recall. This study shows a significant improvement on the accuracy of OpenMP instruction classification by combining sequential and structural features of code text, which will provide a valuable insight into deep learning techniques to programming tasks.
In recent years, there has been a significant rise in the number of software startups globally, driven by advances in technology and increasing reliance on digital solutions. These startups are crucial in shaping the ...
详细信息
Game development education has become more popular all over the World. A review shows several topics, which deal with games in application to teaching and learning: gamification, designing separate courses, and design...
详细信息
Recently, studies related to human activity recognition have developed which have been applied in various fields. In that field Machine learning and deep learning techniques have been widely used for several classific...
详细信息
Skin cancer (SC), particularly melanoma, is a significant health issue due to its high mortality rate when not detected early. This study introduces a novel approach, FMICS: A Robust learning Methodology (Hybrid ANN +...
详细信息
The increasing levels of greenhouse gases and climate change, including phenomena such as El Nino, have caused extreme climatic events such as floods and droughts in various parts of the Indian subcontinent, disruptin...
详细信息
The increasing levels of greenhouse gases and climate change, including phenomena such as El Nino, have caused extreme climatic events such as floods and droughts in various parts of the Indian subcontinent, disrupting the cultivable environment for the crops. This unpredictability poses a challenge for the farmers. The proposed alternate crop recommendation system considers temperature, rainfall, soil, and market data to recommend alternate crops that are both sustainable for the climate and fetch higher prices. The proposed system uses stacking regressor with base learners such as Support Vector Regressor (SVR), Linear Regression (LR), and Random Forest Regressor (RFR) for yield prediction. The Gaussian naive Bayesian algorithm is used to calculate the standard precipitation index, which helps in classifying the drought levels. The experimental results show that the proposed system yields 97% accuracy in predicting yields with minimal root mean square error.
We present a battery-powered wearable system that is able to identify the three basic types of speech disfluencies found in people who stutter: blocks, prolongations, and repetitions. Such a system could be used to ai...
详细信息
ISBN:
(纸本)9798350386394;9798350386400
We present a battery-powered wearable system that is able to identify the three basic types of speech disfluencies found in people who stutter: blocks, prolongations, and repetitions. Such a system could be used to aid speech pathology clinicians by performing automated diagnosis of stuttering or monitoring the progress of speech therapy, tasks that are currently time-consuming and produce potentially unreliable results. The system uses a deep learning model trained on the SEP-28k dataset and deployed on a microcontroller. It performs speech audio acquisition and model inference in real time and stores the inference results to non-volatile memory. Once stored, the results can be further analyzed on a PC and presented to the clinician. Our deep learning model achieved a classification accuracy of 65%, 71%, and 64% for blocks, prolongations, and repetitions, respectively. We discuss the possible applications of this system in speech disorder diagnosis and therapy as well as potential improvements.
The ability to apply knowledge is one of the most important abilities for university students. In the paper, it introduces that how to foster the ability in the teaching of engineering drawing course, and presents som...
详细信息
暂无评论