Longer training times pose a significant challenge in Artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative ...
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Longer training times pose a significant challenge in Artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative to reduce training times in ANNs to enhance the computational efficiency. The initialization of the weights between the layers in ANN plays a vital role in reducing training times. Appropriate weight initialization can help the network converge faster during the training by providing an optimum starting point for the network. Therefore, weight initialization techniques are essential for efficient training of ANNs. This paper revisits and implements different popular weight initialization techniques in ANNs and analyzes their impact on training time. Specifically, this paper implements Gaussian-based, Kaming-based, and Xavier-based weight initiation atop a popular DNN-based network. The experiments are conducted by employing a well-known dataset. The results show that the scenario when no weight initiation is applied consumed the highest training time, whereas different weight initiation techniques contribute in reducing the training times for the network.
The film industry exerts significant economic and cultural influence, and its rapid development is contingent upon the expertise of industry professionals, underscoring the critical importance of film-shooting educati...
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As in the existing opinion summary data set, more than 70% are positive texts, the current opinion summarization approaches are reluctant to generate the negative opinion summary given the input of negative opinions. ...
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Like all software artifacts, metamodels frequently evolve in response to various factors, such as technological advances or changing user requirements. When a metamodel evolves, a simple change can affect all associat...
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The event-triggered scheme (ETS) has been widely used for sensor data scheduling in cyber-physical systems (CPS). Existing literature on the design of ETSs for packet drops deals with the issue of non-Gaussianity of t...
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Aside from pure intellectual interest, why do we teach our students parallel computing? Most people would agree that the primary goal is to produce greater application performance. Yet students frequently parallelize ...
ISBN:
(数字)9798350364606
ISBN:
(纸本)9798350364613
Aside from pure intellectual interest, why do we teach our students parallel computing? Most people would agree that the primary goal is to produce greater application performance. Yet students frequently parallelize code only to discover that it runs disappointingly slower because they don't understand performance. To exploit parallelism effectively, it must operate synergistically with a host of other techniques, including caching, vectorization, algorithms, bit tricks, loop unrolling, using compiler switches, tailoring code to the architecture, exploiting sparsity, changing data representation, metaprogramming, etc. Software performance engineering, which encompasses these techniques, is the science and art of making code run fast or otherwise limiting its consumption of resources, such as energy, memory footprint, network utilization, response time, etc. In this talk, I will argue that the end of Moore's Law makes software performance engineering a critical skill for our students to learn.
People learn causal relations since childhood using counterfactual reasoning. Counterfactual reasoning uses counterfactual examples which take the form of “what if this has happened differently”. Counterfactual exam...
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People learn causal relations since childhood using counterfactual reasoning. Counterfactual reasoning uses counterfactual examples which take the form of “what if this has happened differently”. Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and present counterfactual samples cannot help users gain a deeper understanding of the system. Without a way to verify their understanding, the users can even be misled by such explanations. Such limitations can be overcome through an interactive and iterative framework that allows the users to explore their desired “what-if” scenarios. The purpose of our research is to develop such a framework. In this paper, we present our “what-if” XAI framework (WiXAI), which visualizes the artificial intelligence (AI) classification model from the perspective of the user’s sample and guides their “what-if” exploration. We also formulated how to use the WiXAI framework to generate counterfactuals and understand the feature-feature and feature-output relations in-depth for a local sample. These relations help move the users toward causal understanding.
Breast cancer is a major global health issue, emphasizing the need for accurate diagnostic tools to improve patient outcomes. This paper presents an approach that utilizes Automated Machine Learning (AutoML), specific...
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ISBN:
(数字)9798331509576
ISBN:
(纸本)9798331509583
Breast cancer is a major global health issue, emphasizing the need for accurate diagnostic tools to improve patient outcomes. This paper presents an approach that utilizes Automated Machine Learning (AutoML), specifically the Tree-based Pipeline Optimization Tool (TPOT) Classifier, to streamline the model selection and so that optimizing breast cancer diagnosis process. The objective of the work is to develop a robust predictive model that classifies tumors as benign or malignant based on features derived from medical imaging data. The final model, a Random Forest Classifier, achieved an important accuracy score of 96.49%, demonstrating its effectiveness and reliability in clinical settings. Comprehensive evaluation metrics, including confusion matrix analysis and Receiver Operating Characteristic (ROC) curve assessment, validate the model's performance, highlighting its potential for enhancing breast cancer diagnosis.
This paper aims to address the complex challenge of course assignment for faculty members within a Saudi university, taking into account the socio-cultural constraints imposed by gender-based segregation between stude...
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The Internet of Vehicles (IoV), integrating vehicles with EV charging stations, faces significant vulnerabilities to cyber threats. Designing an efficient Intrusion Detection System (IDS) to detect sophisticated attac...
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