Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks

Monjezi, Verya; Trivedi, Ashutosh; Tan, Gang; Tizpaz-Niari, Saeid

doi:10.1109/icse48619.2023.00136

Cited by 14 publications

(5 citation statements)

References 33 publications

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“…With AI, the cloud platform can monitor the load and dynamically adjust it to improve QoS parameters, optimize power consumption, or reduce infrastructure costs. AI Yunlong and Jie Journal of Engineering and Applied Science (2024) 71:51 encompasses a diverse array of search techniques, including machine learning, reinforcement learning, and planning [43]. Data-intensive tasks in today's world require enhanced intelligence to ensure optimal scheduling decisions, virtual machine migrations, etc., under various constraints [44].…”

Section: Artificial Intelligence-enabled Approachesmentioning

confidence: 99%

Incentive approaches for cloud computing: challenges and solutions

Yunlong,

Jie

2024

J. Eng. Appl. Sci.

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Cloud computing enables highly configurable and reliable computing resources on a rentable per-use scheme, facilitating quick and cost-effective provisioning of large-scale applications. Thanks to the fast-paced evolution of cutting-edge technologies and the rapid spread of cloud-based solutions, the cloud computing ecosystem is now part of our everyday lives. Nevertheless, cloud computing relies on highly sophisticated data centers comprising energy-consuming servers and equipment that require much energy. Stimulating cloud services for active participation and network contributions presents several challenges. Strategies based on artificial intelligence (AI), game theory, and blockchain have great potential to create an economically sustainable cloud ecosystem. This paper explores strategies grounded in AI, game theory, and blockchain to foster an economically sustainable cloud ecosystem. Informed by a survey study, our research delves into incentive approaches within cloud computing. Theoretical foundations, motivations, and enabling techniques are comprehensively examined to provide valuable insights for a broad audience. The primary contributions of this work lie in elucidating the application of AI, game theory, and blockchain to address challenges in incentivizing cloud services, paving the way for a more sustainable and efficient cloud computing landscape.

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Section: Artificial Intelligence-enabled Approachesmentioning

confidence: 99%

Incentive approaches for cloud computing: challenges and solutions

Yunlong,

Jie

2024

J. Eng. Appl. Sci.

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“…ML and DL techniques empower healthcare systems to discern intricate patterns within vast datasets, enabling predictive analytics for disease diagnosis, treatment planning, and health status forecasting [16][17][18]. NN, a subset of ML, simulates the human brain's learning process, aiding in complex data analysis, especially in image recognition and signal processing tasks www.ijacsa.thesai.org within medical imaging and diagnostics [19,20]. FLSs supplements decision-making processes by handling uncertain or imprecise data, crucial in medical scenarios where data might exhibit variability [21].…”

Section: B Literature Studymentioning

confidence: 99%

Efficient Processing of Large-Scale Medical Data in IoT: A Hybrid Hadoop-Spark Approach for Health Status Prediction

Lina,

Wenlong

2024

IJACSA

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In the realm of Internet of Things (IoT)-driven healthcare, diverse technologies, including wearable medical devices, mobile applications, and cloud-based health systems, generate substantial data streams, posing challenges in real-time operations, especially during emergencies. This study recommends a hybrid architecture utilizing Hadoop for real-time processing of extensive medical data within the IoT framework. By employing distributed machine learning models, the system analyzes health-related data streams ingested into Spark streams via Kafka threads, aiming to transform conventional machine learning methodologies within Spark's real-time processing, crafting scalable and efficient distributed approaches for predicting health statuses related to diabetes and heart disease while navigating the landscape of big data. Furthermore, the system provides real-time health status forecasts based on a multitude of input features, disseminates alert messages to caregivers, and stores this valuable information within a distributed database, which is instrumental in health data analysis and the production of flow reports. We compute a range of evaluation parameters to evaluate the proposed methods' efficacy. This assessment phase encompasses measuring the performance of the Spark-based machine learning algorithm in a distributed parallel computing environment.

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“…The timely identification and effective treatment of cancer are paramount for enhancing patient outcomes and curbing disease progression (Mohtasebi et al, 2023). As the landscape of cancer prediction evolves, artificial intelligence (AI) technologies have emerged as powerful tools to streamline this process (Monjezi et al, 2023;Morteza et al, 2023;Rezaei et al, 2023;Zeinali-Rafsanjani et al, 2023). However, challenges persist, particularly in accurately categorizing cancer stages based on gene sets.…”

Section: Introductionmentioning

confidence: 99%

Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study

Amanzholova,

Coşkun

2024

Front. Big Data

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Efficiently detecting and treating cancer at an early stage is crucial to improve the overall treatment process and mitigate the risk of disease progression. In the realm of research, the utilization of artificial intelligence technologies holds significant promise for enhancing advanced cancer diagnosis. Nonetheless, a notable hurdle arises when striving for precise cancer-stage diagnoses through the analysis of gene sets. Issues such as limited sample volumes, data dispersion, overfitting, and the use of linear classifiers with simple parameters hinder prediction performance. This study introduces an innovative approach for predicting early and late-stage cancers by integrating hybrid deep neural networks. A deep neural network classifier, developed using the open-source TensorFlow library and Keras network, incorporates a novel method that combines genetic algorithms, Extreme Learning Machines (ELM), and Deep Belief Networks (DBN). Specifically, two evolutionary techniques, DBN-ELM-BP and DBN-ELM-ELM, are proposed and evaluated using data from The Cancer Genome Atlas (TCGA), encompassing mRNA expression, miRNA levels, DNA methylation, and clinical information. The models demonstrate outstanding prediction accuracy (89.35%−98.75%) in distinguishing between early- and late-stage cancers. Comparative analysis against existing methods in the literature using the same cancer dataset reveals the superiority of the proposed hybrid method, highlighting its enhanced accuracy in cancer stage prediction.

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Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks

Cited by 14 publications

References 33 publications

Incentive approaches for cloud computing: challenges and solutions

Incentive approaches for cloud computing: challenges and solutions

Efficient Processing of Large-Scale Medical Data in IoT: A Hybrid Hadoop-Spark Approach for Health Status Prediction

Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study

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