Human activity recognition systems using wearable sensors is an important issue in pervasive computing, which applies to various domains related to healthcare, context aware and pervasive computing, sports, surveillan...
详细信息
Automatic skin lesion subtyping is a crucial step for diagnosing and treating skin cancer and acts as a first level diagnostic aid for medical experts. Although, in general, deep learning is very effective in image pr...
详细信息
Automatic skin lesion subtyping is a crucial step for diagnosing and treating skin cancer and acts as a first level diagnostic aid for medical experts. Although, in general, deep learning is very effective in image processing tasks, there are notable areas of the processing pipeline in the dermoscopic image regime that can benefit from refinement. Our work identifies two such areas for improvement. First, most benchmark dermoscopic datasets for skin cancers and lesions are highly imbalanced due to the relative rarity and commonality in the occurrence of specific lesion types. Deep learning methods tend to exhibit biased performance in favor of the majority classes with such datasets, leading to poor generalization. Second, dermoscopic images can be associated with irrelevant information in the form of skin color, hair, veins, etc.;hence, limiting the information available to a neural network by retaining only relevant portions of an input image has been successful in prompting the network towards learning task-relevant features and thereby improving its performance. Hence, this research work augments the skin lesion characterization pipeline in the following ways. First, it balances the dataset to overcome sample size biases. Two balancing methods, synthetic minority oversampling TEchnique (SMOTE) and Reweighting, are applied, compared, and analyzed. Second, a lesion segmentation stage is introduced before classification, in addition to a preprocessing stage, to retain only the region of interest. A baseline segmentation approach based on Bi-Directional ConvLSTM U-Net is improved using conditional adversarial training for enhanced segmentation performance. Finally, the classification stage is implemented using EfficientNets, where the B2 variant is used to benchmark and choose between the balancing and segmentation techniques, and the architecture is then scaled through to B7 to analyze the performance boost in lesion classification. From these experiments, we find
In the times of advanced generative artificial intelligence, distinguishing truth from fallacy and deception has become a critical societal challenge. This research attempts to analyze the capabilities of large langua...
详细信息
Expert systems are computer programs that use knowledge and reasoning to solve problems typically solved by human experts. Expert systems have been used in medicine to diagnose diseases, recommend treatments, and plan...
详细信息
Positioning clothing parts ($\mathcal {S} \mathbf{s}$) such as sleeves and collars has been in the realm of manual task that had to be done meticulously in order to prevent unnecessary tanglements during the simulatio...
详细信息
Positioning clothing parts ($\mathcal {S} \mathbf{s}$) such as sleeves and collars has been in the realm of manual task that had to be done meticulously in order to prevent unnecessary tanglements during the simulation. This paper proposes an optimization-based method to computerize the above $\mathcal {S}$-positioning task. For that, we embed each $\mathcal {S}$ to an abstracting cylinder $\mathcal {C}$ such that $\mathcal {S}$-positioning can be done by adjusting only 3$\sim$4 DOFs (e.g., translating/rotating $\mathcal {C}$ or adjusting its radius) instead of per-vertex-full-DOFs. Then, we formulate an objective function E by scoring undesirableness of $\mathcal {S}$'$\mathbf{s}$ position (e.g., $\mathcal {S}$ penetrating the body, $\mathcal {S}$ making cloth-to-cloth intersection). In organizing E into the loop of the Newton's method, the main challenge was to calculate the symbolic gradient and hessian, for which this paper makes several novel contributions. The resultant $\mathcal {S}$-positioning method works quite successfully;$\mathcal {S}$*$\mathbf{s}$ (the output of the S-positioning method ) are tanglement-free thus running the simulator to that configuration produces acceptable draping quickly;Experiments show that, in obtaining acceptable draping, the proposed method produces about ×9.7 speed up compared to when not using it. IEEE
The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an ini...
详细信息
Multimedia data encompasses various modalities, including audio, visual, and text, necessitating the development of robust retrieval methods capable of harnessing these modalities to extract and retrieve semantic info...
详细信息
Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a nov...
详细信息
Food Infestation Detection is more important for food safety and health concerns. It is a challenging task to separate the grains into infested or non-infested. It is found that in the existing system, there is no eff...
详细信息
Due to dynamic smart systems, concept drift in live streaming data is a typical issue, resulting in performance reduction. Despite the fact that there are a variety of traditional ways of handling streaming data, they...
详细信息
暂无评论