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Found 21 papers, 2 papers with code
High-energy physics image classification: A Survey of Jet Applications
In recent times, the fields of high-energy physics (HEP) experimentation and phenomenological studies have seen the integration of machine learning (ML) and its specialized branch, deep learning (DL).
Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities
Anomaly detection in sport facilities has gained significant attention due to its potential to promote energy saving and optimizing operational efficiency.
Edge AI for Internet of Energy: Challenges and Perspectives
The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI).
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection
This paper analyzes and criticizes each method of TL based on image analysis and compares the results of each method, showing that transformers have achieved the best results with an accuracy of 97. 41% for colon cancer detection and 94. 71% for Histopathological Lung cancer.
Federated Learning for Computer Vision
Computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks.
Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions
The paper provides a systematic review of recent research in the field, including data sets and evaluation metrics used in recent studies.
ECG classification using Deep CNN and Gramian Angular Field
This paper study provides a novel contribution to the field of signal processing and DL for ECG signal analysis by introducing a new feature representation method for ECG signals.
On the Sensitivity of Deep Load Disaggregation to Adversarial Attacks
Non-intrusive Load Monitoring (NILM) algorithms, commonly referred to as load disaggregation algorithms, are fundamental tools for effective energy management.
Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic Review
A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content.
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization
Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input feature space and data distribution characteristics.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services.
Blockchain-based Recommender Systems: Applications, Challenges and Future Opportunities
Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc.
An IoT-Based Framework for Remote Fall Monitoring
Fall detection is a serious healthcare issue that needs to be solved.
Smart non-intrusive appliance identification using a novel local power histogramming descriptor with an improved k-nearest neighbors classifier
Non-intrusive load monitoring (NILM) is a key cost-effective technology for monitoring power consumption and contributing to several challenges encountered when transiting to an efficient, sustainable, and competitive energy efficiency environment.
Non-Intrusive Load Monitoring
Computers and Society
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects
Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies.
Appliance-Level Monitoring with Micro-Moment Smart Plugs
Human population are striving against energy-related issues that not only affects society and the development of the world, but also causes global warming.
The emergence of Explainability of Intelligent Systems: Delivering Explainable and Personalised Recommendations for Energy Efficiency
Recommendation systems are intelligent systems that support human decision making, and as such, they have to be explainable in order to increase user trust and improve the acceptance of recommendations.
Appliance identification using a histogram post-processing of 2D local binary patterns for smart grid applications
Identifying domestic appliances in the smart grid leads to a better power usage management and further helps in detecting appliance-level abnormalities.
Improving in-home appliance identification using fuzzy-neighbors-preserving analysis based QR-decomposition
This paper proposes a new appliance identification scheme by introducing a novel approach for extracting highly discriminative characteristic sets that can considerably distinguish between various appliance footprints.
Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations
Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed.
HEMELB Acceleration and Visualization for Cerebral Aneurysms
A weakness in the wall of a cerebral artery causing a dilation or ballooning of the blood vessel is known as a cerebral aneurysm.
