Subject:
All Disciplines Arts & Humanities Biology & Life Sciences Business & Economics Chemistry & Materials Science Computer Science & Mathematics Engineering Environmental & Earth Sciences Medicine & Pharmacology Physical Sciences Public Health & Healthcare Social Sciences
Sort:
Hottest Latest Alphabetical (A-Z) Alphabetical (Z-A)
Type:
All Topic Review Biography
Topic Review
Profit optimization using RFID under an Unreliable SCM
Competition in business is higher in the electronics sector compared to other sectors. In such a situation, the role of a manufacturer is to manage the inventory properly with optimized profit. However, the problem of unreliability within buyers still exists in real world scenarios. The manufacturer adopts the radio frequency identification (RFID) technology to manage the inventory, which can control the unreliability, the inventory pooling effect, and the investment on human labor. For detecting RFID tags, a reasonable number of readers are needed. This study investigates the optimum distance between any two readers when using the optimum number of readers. As a vendor managed inventory (VMI) policy is utilized by the manufacturer, a revenue sharing contract is adopted to prevent the loss of buyers. The aim of this study is to maximize the profits of a two-echelon supply chain management under an advanced technology system. As the life of electronic gadgets is random, it may not follow any specific type of distribution function. The distribution-free approach helps to solve this issue when the mean and the standard deviation are known. The Kuhn-Tucker methodology and classical optimization are used to find the global optimum solution. The numerical analysis demonstrates that the manufacturer can earn more profit in coordination case after utilizing revenue sharing and the optimum distance between readers optimizing cost related to the RFID system. Sensitivity analysis is performed to check the sensibility of the parameters.
  • 209
  • 02 Jun 2023
Topic Review
Alzheimer’s Disease, Machine Learning and Feature Selection Methods
Alzheimer’s disease (AD) is a prevalent form of dementia that accounts for up to 80% of all dementia cases. The use of machine learning and feature selection methods in predicting AD based on gene expression data is a rapidly evolving area of research. 
  • 266
  • 02 Jun 2023
Topic Review
Convolutional Neural Network-Based Layer-Adaptive Ground Control Points Extraction
Ground Control Points (GCPs) are of great significance for applications involving the registration and fusion of heterologous remote sensing images (RSIs). However, utilizing low-level information rather than deep features, traditional methods based on intensity and local image features turn out to be unsuitable for heterologous RSIs because of the large nonlinear radiation difference (NRD), inconsistent resolutions, and geometric distortions. Additionally, the limitations of current heterologous datasets and existing deep-learning-based methods make it difficult to obtain enough precision GCPs from different kinds of heterologous RSIs, especially for thermal infrared (TIR) images that present low spatial resolution and poor contrast.
  • 260
  • 02 Jun 2023
Topic Review
Augmented Reality for Cultural Heritage Communication and Education
Augmented Reality (AR) applications are increasingly used in many research and commercial fields. Some of the fields that these applications are used are recreational digital games, tourism, education, and marketing. Location-based augmented reality is a type of AR that typically runs on mobile devices (smartphones or tablets) and uses the device's GPS sensor to guide users to specific locations. When these locations are reached the users are presented with informative digital content. Location-based augmented reality (AR) applications for cultural heritage communication and education are created to inform the public about cultural heritage monuments in an engaging and entertaining way.
  • 553
  • 02 Jun 2023
Topic Review
Graph Databases
NoSQL databases were created with the primary goal of addressing the shortcomings in the efficiency of relational databases, and can be of four types: document, column, key-value, and graph databases. Graph databases are designed with a different way of representing and storing data, where data is defined as a collection of nodes and edges. Graph databases can store data and relationships efficiently, and have a flexible and easy to understand data schema. 
  • 470
  • 01 Jun 2023
Topic Review
Quantum Machine Learning
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed.
  • 391
  • 01 Jun 2023
Topic Review
Searchable Attribute-Based Encryption Schemes
Given the massive amount of data and the fidelity of cloud servers, adequate security protection and efficient retrieval mechanisms for stored data have become critical problems. Attribute-based encryption brings the ability of fine-grained access control and can achieve a direct encrypted data search while being combined with searchable encryption algorithms.
  • 270
  • 01 Jun 2023
Topic Review
Document-Level Multimodal Sentiment Analysis
An increasing number of people tend to convey their opinions in different modalities. For the purpose of opinion mining, sentiment classification based on multimodal data becomes a major focus. Sentiment analysis at the document level aims to identify the opinion on a main topic expressed by a whole document.
  • 264
  • 01 Jun 2023
Topic Review Peer Reviewed
Large Language Models and Logical Reasoning
In deep learning, large language models are typically trained on data from a corpus as representative of current knowledge. However, natural language is not an ideal form for the reliable communication of concepts. Instead, formal logical statements are preferable since they are subject to verifiability, reliability, and applicability. Another reason for this preference is that natural language is not designed for an efficient and reliable flow of information and knowledge, but is instead designed as an evolutionary adaptation as formed from a prior set of natural constraints. As a formally structured language, logical statements are also more interpretable. They may be informally constructed in the form of a natural language statement, but a formalized logical statement is expected to follow a stricter set of rules, such as with the use of symbols for representing the logic-based operators that connect multiple simple statements and form verifiable propositions.
  • 1.0K
  • 31 May 2023
Topic Review
Deep Learning Techniques
Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields.
  • 355
  • 31 May 2023
  • Page
  • of
  • 371
Featured Entry Collections
>>

Featured Books

>>
Encyclopedia of Social Sciences
Encyclopedia of COVID-19
Encyclopedia of Fungi
Encyclopedia of Digital Society, Industry 5.0 and Smart City
Video Production Service
Feedback