ICMLSC

With a comprehensive review of relevant literature, data, and theories, this study collects post-pandemic data from the Shanghai Stock Exchange 50 Index. Utilizing the Sparse Inverse Covariance Estimation method (GraphicalLassoCV), the research computes ...

This paper presents FedCVD, a federated learning model designed for predicting cardiovascular disease (CVD) by employing logistic regression and Support Vector Machine (SVM) algorithms. FedCVD utilizes the privacy and scalability advantages offered by ...

With the rapidly developing artificial intelligence, metaverse, and 5G applications, the traffic in the Data Center Network exploded in the past decade. Optical switches are implemented to forward packets to reduce the impact of such traffic ...

In the context of developing nations like India, traditional business-to-business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, e-commerce enterprises ...

Software defects are often expensive to fix, especially when they are identified late in development. Packages encapsulate logical functionality and are often developed by particular teams. Package-level defect prediction provides insights into ...

Federated learning can bring significant benefits to edge IoT systems in their scalability, efficiency, and application space by increasing the amount of computing for the nodes while decreasing the amount of network traffic required. On the other hand, ...

The objective of this study was to gain valuable insights into retailer behavior and develop a predictive model to inform their purchasing decisions. This process involved a comprehensive analysis of the various factors that influence retailers when they ...

Generative Adversarial Networks (GANs) exhibit great potential in many areas. In this paper, we explore their potential in multi-step time series forecasting. To the extent of our knowledge, this task has not been extensively researched yet, possibly ...

Benefiting from the rapid development of the modern new energy automobile industry, lithium-ion batteries as the core components of new energy vehicles, the demand is rising. For both industry and consumers, accurately predicting the remaining useful ...

Aiming at the shortcomings of high cost, low efficiency and coarse granularity of traditional consumer demand sequencing methods in practical applications, this paper proposes an online consumer demand ranking and content prediction method based on Kano ...

Interpreting and explaining complex models such as ensemble machine learning models for opinion mining is essential to increase the level of transparency fairness and reliability of positive and negative opinion prediction results. Although ensemble ...

Online learning (OL) from data streams is an emerging area of research that encompasses numerous challenges from stream processing, machine learning, and networking. Stream-processing platforms, such as Apache Kafka and Flink, have basic extensions for ...

Anomaly detection of Cloud POS data plays a significant role in the management activities of the tobacco industry. Effective anomaly detection helps retailers mitigate anomalous losses and optimize business plans. However, existing research related to ...

Crossover is an important process in genetic algorithms. This process will swap genes between the chromosomes of the parents. The results from the crossover process may not be better than those of the parents, which affect the result of the genetic ...

With the increasing variety of computational scenarios and task types in cloud-edge-end collaborative networks, task scheduling in cloud-edge-end collaborative environments can better adapt to various task types and application scenarios, thereby ...

Financial statements are cornerstones of several analyses, such as loan applications, as well as for legal firms collecting evidence and analysis. They exert a significant influence on the decisions of these institutions. Streamlining the processing of ...

Offshore wind turbines (OWTs) installed far from land have historically faced significant maintenance costs and loss of power generation resources due to system failures. As the era of artificial intelligence progresses, predictive and anomaly detection ...

In the application of wireless sensor networks (WSNs), lots of deployed sensor nodes will forward authenticated message to a base station for verification. The technique of data aggregation is thus become important, since it can gain more bandwidth ...

Active noise control (ANC) is a technology that uses sound waves to reduce or eliminate unwanted ambient noise in a given environment. We approached ANC using a deep neural network consisting of convolutional and attention layers, followed by ...

Facial expression recognition (FER) is a burgeoning field within computer vision and artificial intelligence, with significant implications for human-computer interaction and emotion analysis. Recent advancements in deep learning, particularly ...

In this article, a face recognition via thermal imaging: a comparative study of traditional and CNN-based approaches is proposed. The methodology comprises two distinct components: traditional face recognition and CNN-based face recognition. In the ...

Single image super-resolution method using neural networks has achieved remarkable strides. However, most existing works rely on the architecture of Convolutional Neural Networks (CNNs) with shared kernel and the increasing of vertical depth, as result, ...

Coffee leaf disease (CLD) is a major threat to coffee production worldwide, causing significant economic losses for farmers. Accurate and timely estimation of the severity of CLD is crucial for implementing effective control measures. In this paper, we ...

In the past years, since 2020, the outbreak of COVID-19 has alarmed the world with the speed and its spread around the world. This raised the demand of early, accurate and automated detection system for the COVID-19 as there is a scarcity of manpower in ...

Deep learning models, such as YOLOv5, well-known for object detection, and U-Net, used for segmentation, are known for their respective capabilities within computer vision tasks. In this study, the researchers introduced a novel framework that uses ...

Addressing the challenge of overproduction in the tomato industry, this research introduces a unique approach for detecting and classifying the ripeness stages of the Diamante Max tomato variant, utilizing Mask R-CNN and VGG16. By leveraging a self-...

This study outlines the creation of an object detection model utilizing YOLOv5 Small, designed to identify and categorize ambulances on the road, distinguishing them based on various types and characteristics. The research process included the assembly ...

This paper presents a technique for removing unnecessary QR code patterns from captured images of subjects using a U-Net type autoencoder. This study is a part of our series focusing on optical watermarking embedded invisibly in the light illuminating ...

Encryption is a valid means to safeguard the safety of images, and for color images, encryption should be performed considering the intrinsic correlation between R, G, and B components. In this paper, we propose an image encryption algorithm based on a ...

Fingerprints remain constant throughout life. In over 140 years of fingerprint analysis, no two fingerprints have ever been found to be identical, even in identical twins. Each of us is born with a unique set of fingerprints, although experts still don’...