Telecommunications System & Management

ISSN: 2167-0919

Open Access

Volume 10, Issue 3 (2021)

Editorial Pages: 1 - 1

Conference Announcement on Artificial Intelligence and Robotics 2021

Nancy Leo

We are pleased to welcome you to the “International Conference on Robotics and Artificial Intelligence” after the successful completion of the series of Artificial Intelligence 2020. The congress is scheduled to take place in the beautiful city of Miami, USA on November 12-13, 2021. This Artificial Intelligence 2021 conference will provide you with an exemplary research experience and huge ideas.

The perspective of the Artificial Intelligence Conference is to set up transplant research to help people understand how treatment techniques have advanced and how the field has developed in recent years.

Longdom proffers our immense pleasure and honour in extending you a warm invitation to attend Artificial Intelligence 2021 in Miami, USA on November 12-13, 2021. It is focusing on “Innovations and Advancements in Robotics and Artificial Intelligence”, to enhance and explore knowledge among Artificial Intelligence community and to establish corporations and exchanging ideas. Providing the right stage to present stimulating Keynote talks, Plenary sessions, Discussion Panels, B2B Meetings, Poster symposia, Video Presentations and Workshop Artificial Intelligence anticipates over 200 participants around the globe with path breaking subjects, discussions and presentations. This will be a splendid feasibility for the researchers, delegates and the students from Global Universities and Institutes to interact with the world class scientists, speakers, Analyst, practitioners and Industry Professionals.

Longdom all the experts and researchers from the Robotics and Artificial Intelligence sector all over the world to attend “International Conference on Robotics and Artificial Intelligence (Artificial Intelligence 2021) which is going to be held on Miami, USA on November 12-13, 2021. Artificial Intelligence 2021 conference includes Keynote presentations, Oral talks, Poster Presentations, Workshops, and Exhibitors.

The most other engineering majors work with Artificial Intelligence, but the heart of Artificial Intelligence is Automation and Automation Engineering across all the disciples.  Artificial Intelligence 2021 conference is also comprised of Best Post Awards, Best Oral Presentation Awards, Young Researchers Forums (YRF) and also Video Presentation by experts.  We are glad to welcome you all to join and register for the “International Conference on Robotics and Artificial Intelligence” which is going to be held in Miami, USA on November 12-13, 2021.

Short Communication Pages: 2 - 2

AI-based vehicle analytics for smart cities

Venkatesh Wadawadagi

Improving cities is a pressing global need as the world’s population grows and our species becomes rapidly more urbanized. In 1900 just 14 percent of people on earth lived in cities but by 2008 half the world’s population lived in urban areas. Today, 55% of the world’s population lives in urban areas and this percentage is expected to rise to 68% by 2050.

The use of artificial intelligence in smart cities can be life-changing if implemented in the right spaces. There are multiple zones in cities or in urban development where AI can be used to improve the performance and efficiency of the system. AI has the ability to understand how cities are being used and how they are functioning. It assists city planners in comprehending how the city is responding to various changes and initiatives. AI with the help of Deep Learning and Computer Vision has changed the way vehicle analytics is done. With these advancements, vehicle analytics is helping in implementing intriguing solutions like Toll booth automation, Smart parking, Gate security, ATCS (Adaptive Traffic Control System), RLVD (Red Light Violation Detection) etc. This talk starts by briefing about what's AI based vehicle analytics and what all it includes, and goes on to talk about varieties of applications of vehicle analytics including implementation and deployment challenges. Towards the end talk focuses on why it's need of the hour for this populated, industrialised and tech-driven era.

Short Communication Pages: 3 - 3

Artificial Intelligence: Technology Applied on Criminal Justice

Selma Elizabeth Blum

Technology has become an essential aspect of law enforcement routine, helping police officers on solving, preventing and even predicting criminal activity globally. Artificial Intelligence is one of many important tools police can rely on. The harmonic integration between men and machine is now an essential part for operations success on security enforcement. How artificial intelligence can address criminal justice needs? Which innovations we have available to improve public safety? This article will demonstrate how artificial intelligence (AI) has became a major resource in numerous ways. It is now the ultimate solution for criminal justice, based on big data, algorithmics and machine learning to detect different patterns on human behavior. Those solutions are mainly based on pattern identification, image scanning, face recognition, sociodemographic analysis, voice parameters, actions, conducts, movements, biometrics and even emotions acknowledgement, which are now being considered an excellent evidence for deception detection, fraud, violence and terrorists acts. It is also used on DNA documentation, ballistics and profiling. Unlike humans, machines do not tire. On the opposite, it is proven on several ways, to be better than humans. It is confirmed machines are very good on identifying anomalous patterns and learning new patterns faster than humans. AI technologies provide the capacity to detect, predict and evaluate, overcoming errors and present virtuous results. The more amount of data, more precise will be the outcome. AI algorithms can potentially be used as a very efficient observer, increasing the accuracy of police officers on their complex daily routine. Predictive analysis (ex. PREDPOL) is one of many examples we will show to demonstrate how important those solutions subsist and innovate the security context. Those systems process large volumes of information simultaneously, providing precise outcomes. This article will deeply investigate and compare several platforms used by different law enforcement units around the globe, pointing new solutions, challenges and potential developments needed. As a conclusion, we have noticed how important was the introduction of AI on law enforcement routine, performing risk evaluations, crime solutions and delinquency prevention.

Short Communication Pages: 4 - 4

How to accelerate AI in Banks

Ramin Mobasseri

With the exponential rise of AI usage in Banks, many financial organizations are still struggling with building efficient Model Development Life Cycles (MDLC) and the means to expedite business value realization and return of investment (ROI). There are several contributing factors which can give rise in less than optimal MDLC, such as, lack of proper data governance and processes around it as well as lack of performant AI solutions and platforms.

In this session, you will learn how to use most essential business value accelerators (BVA) to expedite Data Science Discovery, Data Ingestion, and Model Development leading to most optimal Model Business integration. This session will provide lots of valuable and real to life strategies and executable plans to help reduce your MDLC and time to market by at least 50%

Sections will include but not limited to:

-               Overview of AI Acceleration in highly regulated environments

-               Effective use of tools and processes in each phase of MDLC

-               Hints and Tips on building effective meta use cases with the lines of businesses, e.g. Fraud, Anti Money Laundering amongst many others

-               Agile Blueprints for Machine Learning (ML) and Natural Language Processing (NLP)

-               Effective Data Governance Policy and Strategy

Short Communication Pages: 5 - 5

Artificial Intelligence-based deep learning techniques for anomaly detection in IoT using the latest IoT23 by Google's Tensorflow2.2


Although numerous profound learning models had been proposed, this research article added to symbolize the investigation of significant deep learning models on the sensible IoT gadgets to perform online protection in IoT by using the realistic Iot-23 dataset. It is a recent network traffic dataset from IoT appliances. IoT gadgets are utilized in various program applications such as domestic, commercial mechanization, and various forms of wearable technologies. IoT security is more critical than network security because of its massive attack surface and multiplied weak spot of IoT gadgets. Universally, the general amount of IoT gadgets conveyed by 2025 is foreseen to achieve 41600 million. So we would like to conduct IoT intrusion and anomaly detection systems of detecting IoT-based attacks by introducing various deep learning models on artificial neural networks such as Recurrent Neural Networks, Convolutional  Neural Networks, Multilayer Perceptron, Supervised GAN Adversarial Network, etc in both binary and multiclass classification modes in IoT- cybersecurity. We generate wide performance metric scores such as Accuracy, false alarm rate, detection rate, loss function, and Mean Absolute error.

Short Communication Pages: 6 - 6

Investigation on Prediction Systems based on LSTM Ô??prediction for dissolved oxygen (DO) in water

Hsuan-Hsuan Chao

Climate change and industrial development have brought greater uncertainty to water resources, and the quality of water has a very significant impact on humans and the entire ecosystem. The current water quality testing relies on the data collected by various monitoring systems, some of which are not immediately available or require more expensive equipment to analyze. Most experts agree that the amount of dissolved oxygen (DO) in the water is the main indicator for judging the quality of water. However, the process of obtaining information is more complicated and cumbersome. If the difficulty of obtaining the information can be simplified, it will make water resources better. Management is more efficient.

In recent years, artificial intelligence is often developed to assist in many complex decision-making tasks. We develop a prediction model based on LSTM. We design a machine learning model and provide a large amount of data to make it find the rules and learn from it. Improve the predictive ability of the model. Through the model, the water quality can be monitored and analyzed, and the data obtained can be used to judge and predict the water quality state and deal with water pollution problems in time.

Short Communication Pages: 7 - 7

Artificial Intelligence in Cyber Security for Industry 4.0

Farah Jemili

The recent White House report on artificial intelligence (AI) highlights the importance of AI and the need for a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need to systematically develop and implement AI to see its real impact in the next generation of industrial systems, known as Industry 4.0. This article provides an overview of the current state of AI in industrial applications and offers our contribution to the deployment of AI in cybersecurity for Industry 4.0.

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