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Topic Review
Fault Detection Approaches for Lithium-Ion Batteries
Battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. Researchers used Mahalanobis distance (MD) and independent component analysis (ICA) to detect early battery faults in a real-world energy storage system (ESS). The fault types included historical data of battery overvoltage and humidity anomaly alarms generated by the system management program. These are typical preliminary symptoms of thermal runaway, the leading cause of lithium-ion battery fires. The alarms were generated by the system management program based on thresholds. If a fire occurs in an ESS, the humidity inside the ESS will increase very quickly, which means that threshold-based alarm generation methods can be risky. In addition, industrial datasets contain many outliers for various reasons, including measurement and communication errors in sensors. These outliers can lead to biased training results for models. 
  • 131
  • 18 Feb 2024
Topic Review
The Existing Remote Sensing Index Resources
Remote sensing indices are widely used in various fields of geoscience research. However, there are limits to how effectively the knowledge of indices can be managed or analyzed. One of the main problems is the lack of ontology models and research on indices, which makes it difficult to acquire and update knowledge in this area. 
  • 131
  • 18 Feb 2024
Topic Review
Opium Poppy Detection in Unmanned Aerial Vehicle Imagery
Opium poppy is a medicinal plant, and its cultivation is illegal without legal approval in China. Unmanned aerial vehicle (UAV) is an effective tool for monitoring illegal poppy cultivation. Unmanned aerial vehicle (UAV) is more flexible and mobile than remote sensing satellite, and their high-resolution images can help to detect poppies in areas that are hard to see. 
  • 182
  • 18 Feb 2024
Topic Review
Activation-Based Pruning of Neural Networks
A novel technique is presented for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. The technique is based on the number of times each neuron is activated during model training. Further analysis demonstrated that activation-based pruning can be considered a dimensionality reduction technique, as it leads to a sparse low-rank matrix approximation for each hidden layer of the neural network. The rank-reduced neural network generated using activation-based pruning has better accuracy than a rank-reduced network using principal component analysis. After each successive pruning, the amount of reduction in the magnitude of singular values of each matrix representing the hidden layers of the network is equivalent to introducing the sum of singular values of the hidden layers as a regularization parameter to the objective function.
  • 228
  • 17 Feb 2024
Topic Review
AI-Driven Sentiment Analysis of Amazon Reviews Using BERT
Understanding customer emotions and preferences is paramount for success in the dynamic product design landscape. The pre-trained Bidirectional Encoder Representation from Transformers (BERT) model and the Text-to-Text Transfer Transformer (T5) are deployed to predict customer emotions. These models were trained on synthetically generated and manually labeled datasets to detect the specific features from review data, then sentiment analysis was performed to classify the data into positive, negative, and neutral reviews concerning their aspects. 
  • 213
  • 17 Feb 2024
Topic Review
Click-Through Rate Prediction and Customer Representation
Click-Through Rate Prediction is a significant subject in e-commerce for both academia and industry. In order to accurately predict the customer's click intent, it is necessary to create a personalized customer representation. Learning such a customer representation is currently state-of-the-art.
  • 157
  • 14 Feb 2024
Topic Review
High-Fidelity Synthetic Face Generation for Rosacea Skin Condition
Similarly to the majority of deep learning applications, diagnosing skin diseases using computer vision and deep learning often requires a large volume of data. However, obtaining sufficient data for particular types of facial skin conditions can be difficult, due to privacy concerns. As a result, conditions like rosacea are often understudied in computer-aided diagnosis. The limited availability of data for facial skin conditions has led to the investigation of alternative methods of computer-aided diagnosis. Generative adversarial networks (GANs), mainly variants of StyleGANs, have demonstrated promising results in generating synthetic facial images.
  • 122
  • 14 Feb 2024
Topic Review
Image-Compression Techniques
Image compression is a vital component for domains in which the computational resources are usually scarce such as automotive or telemedicine fields. Also, when discussing real-time systems, the large amount of data that must flow through the system can represent a bottleneck. Therefore, the storage of images, alongside the compression, transmission, and decompression procedures, becomes vital. In recent years, many compression techniques that only preserve the quality of the region of interest of an image have been developed, the other parts being either discarded or compressed with major quality loss. 
  • 215
  • 08 Feb 2024
Topic Review
Anomaly Detection in Software-Defined Networks
One solution enabling the implementation of modern methods for managing and monitoring telecommunication networks is the concept of software-defined networks (SDN). It introduces a centralized architecture for managing computer networks that is fully programmable. With the increasing availability of computational power, contemporary machine learning has undergone a paradigm shift, placing a heightened emphasis on deep learning methodologies. The pervasive automation of various processes necessitates a critical re-evaluation of contemporary network implementations, specifically concerning security protocols and the imperative need for swift, precise responses to system failures.
  • 153
  • 08 Feb 2024
Topic Review
Incremental Scene Classification Using Dual Knowledge Distillation
Conventional deep neural networks face challenges in handling the increasing amount of information in real-world scenarios where it is impractical to gather all the training data at once. Incremental learning, also known as continual learning, provides a solution for lightweight and sustainable learning with neural networks.
  • 244
  • 08 Feb 2024
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