Link optimization of the new generation instant messaging network based on artificial intelligence technology

2020 ◽  
pp. 1-12
Author(s):  
Xia Xu
Keyword(s):  
Artificial Intelligence ◽  
Communication Network ◽  
Large Scale ◽  
Instant Messaging ◽  
Communication Link ◽  
Intelligent Algorithms ◽  
Practical Applications ◽  
Evolution Algorithm ◽  
Intelligent Routing ◽  
Instant Communication

The current network environment is dynamic, open and extensible. In order to better ensure the needs of users, higher requirements are placed on link resource allocation. Based on the research and analysis of the instant communication protocol, this paper studies an intelligent routing evolution algorithm and related fault recovery strategy for the instant communication network. Research on instant messaging intelligent algorithms for routing evolution is mainly based on routing algorithms and artificial intelligence intelligent algorithms. When a link failure occurs in the communication network, the routing algorithm performs route reconstruction and optimization on the entire instant communication network. Considering that there may be evolutionary needs of large-scale routing networks in practical applications, this paper introduces artificial intelligence intelligent algorithms to optimize intelligent algorithms to improve efficiency. A cognitive routing protocol based on MIMO (Multiple Input Multiple Output) technology is proposed. By using MIMO technology, a lot of gain is brought to the communication link under multiple antennas. These gains correspond to different link types. The protocol realizes cognition through intelligent routing evolution algorithm and predicts the state of the network. Setting the routing life and hello period according to the perceived network status can optimize the performance of the network.

scholarly journals Secured authentication using CAPTCHA and visualized passcode

2018 ◽  
Vol 7 (1.8) ◽  
pp. 72
Author(s):  
C Narasimha ◽  
M Sreedevi
Keyword(s):  
Artificial Intelligence ◽  
Large Scale ◽  
Internet Technology ◽  
Related Services ◽  
Practical Applications ◽  
Privacy Requirements ◽  
Privacy Model ◽  
Privacy Issues ◽  
Dictionary Attacks

Numerical harms initiate many privacy characteristics like cryptography. But Artificial intelligence is the best aid for the current privacy requirements, still not properly applied for privacy issues. Now we introduce a new privacy model of privacy that uses Captcha model, in our privacy model we use both the Captcha and a visualized pass code. This model tolerates from most of the privacy attacks like dictionary attacks, keyboard logging attacks, forwarding methods, search set methods etc., This model is well suitable for either a small or large scale applications, the primary intention is improving privacy in internet technology and related services. In this methodology solving a Captcha is a challenge in every login. Finally to improve privacy for practical applications this technique is efficient.

We Need Algorithms That Can Make Explicit What Is Implicit

2017 ◽  
Author(s):  
Bernardo Cuenca Grau ◽  
Adolfo Plasencia
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Knowledge Representation ◽  
Computer Science ◽  
Health Systems ◽  
Large Scale ◽  
Practical Applications ◽  
University Of Oxford ◽  
Comprehensive Health ◽  
Structured Information

In this dialogue, Bernardo Cuenca Grau, a computer scientist at the Department of Computer Science, University of Oxford, begins by explaining his research in technology based on ontologies and knowledge representation, somewhere between mathematics, philosophy, and computer science. He goes on to argue why we need to represent knowledge in a way that it can be processed by a computer and therefore enable automated reasoning of this knowledge using artificial intelligence. Later he explains how his investigation probes the limits of mathematics to find the most appropriate languages for developing practical applications. For example, the large-scale processing of structured information linked to comprehensive health systems. Bernardo is supportive of collective tools such as Wikipedia. He also discusses why in his opinion the success of a scientific or technological idea depends very much on luck, and why the semantic web has not been defined. Furthermore, he argues why bureaucracy confuses process with progress.

Bullying, School Violence, and Climate in Evolving Contexts

2018 ◽  
Author(s):  
Ron Avi Astor ◽  
Rami Benbenisthty
Keyword(s):  
School Violence ◽  
School Safety ◽  
Media Coverage ◽  
Large Scale ◽  
Ecological Model ◽  
Empirical Studies ◽  
Practical Applications ◽  
Teacher Student ◽  
Area Of Interest ◽  
New Research

Since 2005, the bullying, school violence, and school safety literatures have expanded dramatically in content, disciplines, and empirical studies. However, with this massive expansion of research, there is also a surprising lack of theoretical and empirical direction to guide efforts on how to advance our basic science and practical applications of this growing scientific area of interest. Parallel to this surge in interest, cultural norms, media coverage, and policies to address school safety and bullying have evolved at a remarkably quick pace over the past 13 years. For example, behaviors and populations that just a decade ago were not included in the school violence, bullying, and school safety discourse are now accepted areas of inquiry. These include, for instance, cyberbullying, sexting, social media shaming, teacher–student and student–teacher bullying, sexual harassment and assault, homicide, and suicide. Populations in schools not previously explored, such as lesbian, gay, bisexual, transgender, and queer students and educators and military- and veteran-connected students, become the foci of new research, policies, and programs. As a result, all US states and most industrialized countries now have a complex quilt of new school safety and bullying legislation and policies. Large-scale research and intervention funding programs are often linked to these policies. This book suggests an empirically driven unifying model that brings together these previously distinct literatures. This book presents an ecological model of school violence, bullying, and safety in evolving contexts that integrates all we have learned in the 13 years, and suggests ways to move forward.

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals 2D Nanomaterials for Effective Energy Scavenging

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Md Al Mahadi Hasan ◽  
Yuanhao Wang ◽  
Chris R. Bowen ◽  
Ya Yang
Keyword(s):  
Large Scale ◽  
Waste Heat ◽  
Material Selection ◽  
Power Supplies ◽  
Small Scale ◽  
Energy Scavenging ◽  
Power Sources ◽  
Practical Applications ◽  
2D Nanomaterials ◽  
Self Powered

AbstractThe development of a nation is deeply related to its energy consumption. 2D nanomaterials have become a spotlight for energy harvesting applications from the small-scale of low-power electronics to a large-scale for industry-level applications, such as self-powered sensor devices, environmental monitoring, and large-scale power generation. Scientists from around the world are working to utilize their engrossing properties to overcome the challenges in material selection and fabrication technologies for compact energy scavenging devices to replace batteries and traditional power sources. In this review, the variety of techniques for scavenging energies from sustainable sources such as solar, air, waste heat, and surrounding mechanical forces are discussed that exploit the fascinating properties of 2D nanomaterials. In addition, practical applications of these fabricated power generating devices and their performance as an alternative to conventional power supplies are discussed with the future pertinence to solve the energy problems in various fields and applications.

scholarly journals Application of Novel Non-Thermal Physical Technologies to Degrade Mycotoxins

2021 ◽  
Vol 7 (5) ◽  
pp. 395
Author(s):  
Mohammad Yousefi ◽  
Masoud Aman Mohammadi ◽  
Maryam Zabihzadeh Khajavi ◽  
Ali Ehsani ◽  
Vladimír Scholtz
Keyword(s):  
Electron Beam ◽  
Food Systems ◽  
Large Scale ◽  
Plasma Electron ◽  
Pulsed Light ◽  
Beam Radiation ◽  
Practical Applications ◽  
Electron Beam Radiation ◽  
Non Thermal Plasma ◽  
Agriculture And Food

Mycotoxins cause adverse effects on human health. Therefore, it is of the utmost importance to confront them, particularly in agriculture and food systems. Non-thermal plasma, electron beam radiation, and pulsed light are possible novel non-thermal technologies offering promising results in degrading mycotoxins with potential for practical applications. In this paper, the available publications are reviewed—some of them report efficiency of more than 90%, sometimes almost 100%. The mechanisms of action, advantages, efficacy, limitations, and undesirable effects are reviewed and discussed. The first foretastes of plasma and electron beam application in the industry are in the developing stages, while pulsed light has not been employed in large-scale application yet.

scholarly journals A Short Review on Recent Advances of Hydrogel-Based Adsorbents for Heavy Metal Ions

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 864
Author(s):  
Suguna Perumal ◽  
Raji Atchudan ◽  
Thomas Nesakumar Jebakumar Immanuel Edison ◽  
Rajendran Suresh Babu ◽  
Petchimuthu Karpagavinayagam ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Large Scale ◽  
Water Bodies ◽  
Dimensional Structure ◽  
High Porosity ◽  
Adsorption Method ◽  
Toxic Heavy Metal ◽  
Practical Applications

The growth of industry fulfills our necessity and promotes economic development. However, pollutants from such industries pollute water bodies which pose a high risk for living organisms. Thus, researchers have been urged to develop an efficient method to remove toxic heavy metal ions from water bodies. The adsorption method shows promising results for the removal of heavy metal ions and is easy to operate on a large scale, thus can be applied to practical applications. Numerous adsorbents were developed and reported, among them hydrogels, which attract great attention because of the reusability, ease of preparation, and handling. Hydrogels are generally prepared by the cross-linking of polymers that result in a three-dimensional structure, showing high porosity and high functionality. They are hydrophilic in nature because of the functional groups, and are non-toxic. Thus, this review provides various methods of hydrogel adsorbents preparation and summarizes recent progress in the use of hydrogel adsorbents for the removal of heavy metal ions. Further, the mechanism involved in the removal of heavy metal ions is briefly discussed. The most recent studies about the adsorption method for the treatment of heavy metal ions contaminated water are presented.

scholarly journals Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

2020 ◽  
Vol 34 (01) ◽  
pp. 630-637 ◽  
Author(s):  
Ferdinando Fioretto ◽  
Terrence W.K. Mak ◽  
Pascal Van Hentenryck
Keyword(s):  
Deep Learning ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Large Scale ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Practical Applications ◽  
Fundamental Building Block ◽  
Optimal Power

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

A Model-Based Factored Bayesian Reinforcement Learning Approach

2014 ◽  
Vol 513-517 ◽  
pp. 1092-1095
Author(s):  
Bo Wu ◽  
Yan Peng Feng ◽  
Hong Yan Zheng
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Iteration Algorithm ◽  
Value Iteration ◽  
Practical Applications ◽  
Model Based ◽  
Online Planning ◽  
Bayesian Reinforcement Learning ◽  
Bayesian Inference Method ◽  
Unknown Structure

Bayesian reinforcement learning has turned out to be an effective solution to the optimal tradeoff between exploration and exploitation. However, in practical applications, the learning parameters with exponential growth are the main impediment for online planning and learning. To overcome this problem, we bring factored representations, model-based learning, and Bayesian reinforcement learning together in a new approach. Firstly, we exploit a factored representation to describe the states to reduce the size of learning parameters, and adopt Bayesian inference method to learn the unknown structure and parameters simultaneously. Then, we use an online point-based value iteration algorithm to plan and learn. The experimental results show that the proposed approach is an effective way for improving the learning efficiency in large-scale state spaces.

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