Biography:

Joy I. Z. Chen was born in Taiwan. He received his B.S. degree in electronic engineering from National Taiwan Technical University, Taipei, Taiwan, in 1985ï¼› M.S. degree in electrical engineering from Da-Yeh University, Chang Hua, Taiwan, in 1995; and the Ph. D. degree in electrical engineering from National Defense University, Tao-Yuan, Taiwan, in 2001. He is currently a distinguished professor in the Department of Electrical Engineering at Da-Yeh University, Chang-Hua Taiwan R.O.C. Prior to joining the Da-Yeh University faculty, he worked at the Control Data Company (Taiwan) as a technical manger from September 1985 to September 1996. His research interests include wireless communications, communication theory, communication statistics, and technical spread spectrum.        

Abstract:

To govern the interaction and the development of applications over the IoT (internet of things) environment without considering the security is still a concerned issue nowadays. The study on huge of security issues relevant to the application layer of IoT is becoming urgently. On the other hand, it is important for addressing the development of security algorithm to protect the IoT system from malicious attack. In this article, a fusion diversity scheme adopts both MRC (maximum ratio combining) and SC (selection combining) schemes with BF (belief propagation) algorithm are proposed. In MRC stage, specified parameters first extracted and before combined with the control information they weighted by one estimation value. The fused information results from MRC forward to the SC to generate the final trust value for making the decision. However, there is a final stage of BF adopted as the role provides with a guaranteed QoS over IoT architecture. The simulation results from experiments deployed with physical assessment show that the security has more reliability after the fusion of MRC/SC schemes and BP (belief propagation) algorithm for the TM procedure. In fact, there are many existing traditional solutions developed to count different attacks for the computer network or for each layer of the internet. For example, the encryption of information used for confidentiality and there are many other most popular cipher algorithms, such as RSA (Rivest Shamir Adleman), ECC (error correlation code), AES (advanced encryption standard), 3DES (triple data encryption algorithm). Based on the aforementioned points that motivates the paper propose an algorithm to fuse the scheme of TM with a famous linear diversity method, MRC (maximum ratio combining), for IoT security. The proposed fusion diversity scheme is more reliable in terms of security validated by the simulation. Alternatively, the reducing QoS degree for the requester is the penalty.

Biography:

Sourav Rout is a senior management professional, having worked for most part of his decade-old career in telecommunications, with additional exposure to cyber security and data science industries. Currently with the Telenet Group in Brussels, he contributes to the Internet of Things initiative, long-term technology strategy and enhancing operational excellence. His experience spans strategy, analytics, partnerships and business process management.

Sourav obtained the MBA from the Indian Institute of Management Bangalore and MS in Telecoms from Telecom ParisTech, France. Happily married, he finds strength in the company of his wife and they enjoy taking long walks and listening to music.
 

Abstract:

The Internet of Things is causing a proliferation of heterogenous data acquisition points (such as devices and sensors). As companies struggle to understand and make use of the data so acquired for sustained growth and monetization, governance of this data holds key. Not all the data is good or usable; and not every data will be used.

It is important for companies to understand why they need the data they want to acquire, how will they treat this data and who will use them. This presentation touches upon the organizational preparedness with regards to data processing and utilization, and outlines the challenges companies face in framing a data governance model. It then goes on to propose a template framework that can be utilized.

Finally, with the help of key uses cases (in telecoms) built around AI and Machine Learning, the author emphasizes on the need of having a well structured data governance policy that help build up such use cases that facilitate maintaining competitive advantage and customer delight.

Biography:

Hansal is a Computer Science and Engineering student who has completed his 2nd year from Pandit Deendayal Petroleum University. Multidisciplinary research topics interest him. He is a cisco certified network associate (CCNA) and a cisco certified network professional (CCNP) in routing and switching. He is a fellowship holder of the premier research scholarship of India—the KVPY Fellowship. He has presented more than 3 papers in reputed conferences at national level and has been helpful to multiple fellow students for their research interests. He is also a co-founder of an IT based start-up that is under incubation under central government funding scheme.

Abstract:

To solve these problems we have tried to mount a fuzzy logic based inference system to classify the queuing-incoming packets (voice, video and text). Network management is becoming increasingly essential with the acute rise in the number of applications that use computer networks and the advent of ubiquitous internet access. Thus availability, integrity and competency of computer networks becomes a priority today and a crucial resource to be managed. To assess the effectiveness of these networks, the traffic parameters need to be analysed.

This paper attempts to apply a fuzzy logic scheme --based on descriptors like energy, centrality, concentration etc. to recognise whether an instance represents an anomaly or not. Indeed the paper proposes an intelligent system with the capability to monitor the network's traffic (specifically VoIP) flow. The proposed anomaly detection system exposes network problems autonomously issuing alarms when a possible problem is present.

Biography:

Abstract:

Artificial intelligence is the use of computer systems to develop systems that can perform human tasks, and mimic humans for example through Visual perception, speech recognition, language translation and decision-making processes. Artificial intelligence is key to the development of media businesses, where an example of Google assistant is now a familiar feature in most business. In the digital media, Artificial intelligence tools can be used to differentiate different publishers in a crowded market by sending the correct message to the right clients at the set time. Media companies currently use Facebook and Google to transmit news information; this has enabled the businesses to fight fairly on the same platform. The digital media then provides to the user's relevant news that they desire at that time, like when you want to buy a new phone the systems can select a list and give you better options to choose from as from low prices to those discounted (Iliopoulos, 2017).

Currently, Artificial Intelligence is being used on programmatic media where they are using a demand-side platform (DSP) such as Google DoubleClick Bid Manager. Some of the keys feature brought to the business by Artificial Intelligence includes simplified procurement and buying process, real-time reporting, automated budget, and real-time bidding models that all contribute towards improving business performance. What it has is also implemented in doing business analytics and business engineering, where the media like social media will act as a workbench to collect user information of preferences and choices; it will also analyze the business competitiveness in the markets and lastly compare the business with other important companies of similar types. From the data collected the business can easily strategize or re-strategize on their business goals and work towards making more profits (Porter, 2018).

Artificial Intelligence tools used currently in the media include the: -

1.      Google AdWords that optimizes the search engines such that using machine learning technology adverts will be displayed that might interest the users, based on their keywords, search item or device and your location.

2.      Google Analytics and Pave AI are used for business analytics where they can analyze user data and give a plan to perform a strategic action. Pave AI uses graphs and charts to recommend actions and recommendations.

3.      Facebook AI research helps communities to understand the importance of system automation and use of AI influenced advertisements.

Future impacts of AI to the media includes increases in job performances, reduced time of performing transactions and association of robots and humans working together to achieve organizations objectives. Other contributions of AI are the reduction in menial and tedious jobs and physically strenuous tasks. But all are not positive; some of the disadvantages include loss of employment by a replacement for more useful machines.

In conclusion, AI is key to the next phase of media where the business will compete in the similar platform with no bias, as long as they stick to the required rules and guidelines. Companies need to use the media to market themselves and their brands out there for others to see and make their conclusions on the products.