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Found 31 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).
Machine Learning and Vision Transformers for Thyroid Carcinoma Diagnosis: A review
The growing interest in developing smart diagnostic systems to help medical experts process extensive data for treating incurable diseases has been notable.
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey
DTL allows high-performance models using small yet related datasets, FL enables training on confidential data without dataset possession, and RL optimizes decision-making in dynamic environments, reducing computation costs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
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.
Enhancing Kinship Verification through Multiscale Retinex and Combined Deep-Shallow features
The challenge of kinship verification from facial images represents a cutting-edge and formidable frontier in the realms of pattern recognition and computer vision.
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).
Current Topological and Machine Learning Applications for Bias Detection in Text
Institutional bias can impact patient outcomes, educational attainment, and legal system navigation.
Hybrid Whale-Mud-Ring Optimization for Precise Color Skin Cancer Image Segmentation
Timely identification and treatment of rapidly progressing skin cancers can significantly contribute to the preservation of patients' health and well-being.
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.
AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions
There has been a growing interest in creating intelligent diagnostic systems to assist medical professionals in analyzing and processing big data for the treatment of incurable diseases.
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.
Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions
Chat Generative Pre-trained Transformer (ChatGPT) has gained significant interest and attention since its launch in November 2022.
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.
Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality
However, hot spot and snail trails in PV modules caused mostly by crakes reduce their efficiency and power capacity.
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.
Improving CNN-based Person Re-identification using score Normalization
For example, without normalization, the rank-20 rate accuracies of the GRID, CUHK01, VIPeR and PRID450S datasets were 61. 92%, 83. 90%, 92. 03%, 96. 22%; however, after score normalization, they have increased to 64. 64%, 89. 30%, 92. 78%, and 98. 76%, respectively.
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)
+2
Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review
The algorithms and methods used in several studies are presented, and the principles of DTL-based IDS subcategories are presented to the reader and illustrated deeply and clearly
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.
Panoptic Segmentation: A Review
Image segmentation for video analysis plays an essential role in different research fields such as smart city, healthcare, computer vision and geoscience, and remote sensing applications.
Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations
Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results.
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.
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
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.
