Title: Optimizing financial technical indicators: Evolutionary learning approach in optimizing advertise learning experts

Abstract:

Within the framework of expert advice learning strategies, one needs to have good enough experts in terms of performance, causality, and stability. Indeed, an expert advice online learning algorithm is an algorithm, which deduces its prediction from the advice of its experts. Having well-performing experts increases the performance of our algorithm; therefore, it is necessary to improve their performance. In order to achieve this objective, we were able to create an objective function, which reflects the performance of our experts and we created a causality inspired by brain neurons causality. In the case of our experts (technical indicators), we cannot determine with certainty the regularity of our objective functions which differs from one expert to another. This lack of information on regularity and a large number of functions to be optimized has pushed us to see beyond classical convex optimization and to think of a type of optimization, evolutionary learning.

Biography:

Chaouachi Wassim has completed his Master's degree at the age of 24 years from Ecole Normale supérieure and Paris-Dauphine University in Applied Mathematics and Machine Learning. He is a Quantitative Portfolio Manager at one of the best hedge funds in Europe. He is Co-Founder and CEO of Richmond Analytica, a company providing AI-based investment strategies in the cryptocurrencies universe

Title: Prediction of environmental indicators in land levelling using artificial intelligence techniques

Abstract:

This work aimed to determine the best linear model Adaptive Neuro-Fuzzy Inference System (ANFIS) and Sensitivity Analysis to predict the energy consumption for land leveling. In this research effects of various soil properties such as Embankment Volume, Soil Compressibility Factor, Specific Gravity, Moisture Content, Slope, Sand Percent, and Soil Swelling Index in energy consumption were investigated. The study consisted of 90 samples were collected from 3 different regions. The grid size was set at 20 m in 20 m (20*20) from farmland in the Karaj province of Iran. The values of RMSE and R2 derived by the ICA-ANN model were, to Labor Energy (0.0146 and 0.9987), Fuel energy (0.0322 and 0.9975), Total Machinery Cost (0.0248 and 0.9963), Total Machinery Energy (0.0161 and 0.9987) respectively, while these parameters for multivariate regression model were, to Labor Energy (0.1394 and 0.9008), Fuel energy (0.1514 and 0.8913), Total Machinery Cost (TMC) (0.1492 and 0.9128), Total Machinery Energy (0.1378 and 0.9103). Respectively, while these parameters for the ANN model were, to Labor Energy (0.0159 and 0.9990), Fuel energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy (0.0157 and 0.9990) respectively, while these parameters for Sensitivity analysis model were, to Labor Energy (0.1899 and 0.8631), Fuel energy (0.8562 and 0.0206), Total Machinery Cost (0.1946 and 0.8581), Total Machinery Energy (0.1892 and 0.8437) respectively, respectively, while these parameters for ANFIS model were, to Labor Energy (0.0159 and 0.9990), Fuel energy (0.0206 and 0.9983), Total Machinery Cost (0.0287 and 0.9966), Total Machinery Energy (0.0157 and 0.9990), Results showed that ICA_ANN with seven neurons in hidden layer had better. According to the results of Sensitivity Analysis, only three parameters; Density, Soil Compressibility Factor and, Embankment Volume Index had a significant effect on fuel consumption. According to the results of regression, only three parameters; Slope, Cut-Fill Volume (V) and, Soil Swelling Index (SSI) had a significant effect on energy consumption. Using an adaptive neuro-fuzzy inference system for the prediction of labor energy, fuel energy, total machinery cost, and total machinery energy can be successfully demonstrated.

Biography:

Alzoubi has completed his Ph.D. at the age of 40 years at Tehran University and postdoctoral studies from Tehran University School of Surveying Geospatial Engineering-Department of Surveying and Geomatics Engineering. He is the director at the Directorate of Engineering and Transportation, a premier service organization. He has published more than 15 papers in reputed journals and has been serving as an editorial board member of repute. He Opening and studying the financial offers and the organization of the fundamental record, supervising the efficiency of electrical generators at the Nseeb border center, and Supervising the efficiency of agricultural machinery at the ministry of agriculture.

Title: Third millennium lifesaving smart cyberspace driven by AI & robotics

Abstract:

Given the current dynamic developments in the field of AI & Robotics, Big Data, Massive Data Storage and Ubiquitous access to highspeed Internet 24/7 for anyone worldwide, the term Smart Cyberspace is becoming a well-accepted reality. In light of the currently ongoing developments of the Covid19 crisis, having the effective real-time application of Artificial Intelligence & Robotics with the Big Data remotely control via the Internet is essential. The utilization of John Hopkins Corona Map [1], in conjunction with collecting real-time data from the Electronic Health Record (EHR) in the nation and worldwide, as well as collections of antibodies contributes well to community worldwide aspirations to save human lives and to restart the economies worldwide. These are the most dramatic times for mankind worldwide, and yet despite its most negative impact it does also inspire dynamic innovation, research, and developments in the world of health, business, government, industry, plus., while promoting the seamless creation of multidisciplinary teams of experts in the nation and worldwide. The authors will discuss the current and future dynamic trends in research, innovation, and developments of cutting-edge technologies, AI. Robotics and smart cyber systems will contribute effectively to people saving lives, and decision-makers in the nation and worldwide.

Biography:

Professor Dr. Eduard Babulak is an accomplished international scholar, researcher, consultant, educator, professional engineer and polyglot, with more than thirty years of experience. He served as successfully published and his research was cited by scholars all over the world. He serves as Chair of the IEEE Vancouver Ethics, Professional and Conference Committee. He was Invited Speaker at the University of Cambridge, MIT, Purdue Speaker Photo University, Yokohama National University and the University of Electro-Communications in Tokyo, Japan, Shanghai Jiao Tong University, Sungkyunkwan University in Korea, Penn State in the USA, Czech Technical University in Prague, University at West Indies, Graz University of Technology, Austria, and other prestigious academic institutions worldwide. His academic and engineering work was recognized internationally by the Engineering Council in the UK, the European Federation of Engineers, and credited by the Ontario Society of Professional Engineers and APEG in British Columbia in Canada. He was awarded a higher postdoctoral degree DOCENT – Doctor of Science (D.Sc.) in the Czech Republic, Ph.D., M.Sc., and High National Certificate (HNC) diplomas in the United Kingdom, as well as, the M.Sc., and B.Sc. diplomas in Electrical Engineering Slovakia. He serves as the Editor-in-Chief, Associate Editor-in-Chief, Co-Editor, and Guest-Editor. He speaks 16 languages and his biography was cited in the Cambridge Blue Book, Cambridge Index of Biographies, Stanford Who’s Who, and a number of issues of Who’s Who in the World and America

Title: Delay induced swarm pattern bifurcations in mixed reality experiments

Abstract:

Statement of the Problem:  Natural swarms exhibit patterns in a variety of forms and have inspired researchers to understand how simple organisms produce complex, emergent patterns occurring when individual organisms follow simple dynamics and local rules. Our work provides a model for swarming behavior of coupled mobile agents with communication-time delay which exhibits multiple dynamic patterns in space, which depend on interaction strength and communication delay. Methodology & Theoretical Orientation: A thorough bifurcation analysis has been carried out to explore parameter regions where various patterns occur. We extend this work to robotics applications by introducing a mixed-reality framework in which real and simulated robots communicate in real time creating the self-organized states predicted by the theory. The mixed-reality framework allows for systematic and incremental introduction of real-world complexity by coupling a few real robots and a large number of idealized (virtual) robots together in a swarm - the latter being well understood. Findings: The proposed swarm controller was tested on two different robotic platforms: NRL’s autonomous air vehicles and UPENN’s micro-autonomous surface vehicles on water. Theoretical pattern formation results are confirmed in mixed-reality experiments. Conclusion & Significance: Increased understanding of challenges for real robots is obtained as a systematic, incremental verification of swarming behavior at low cost and risk of damage.  Switching between patterns is achieved in the hybrid experiments, thus simulating flexibile behavior of the real robotic system.

Biography:

Ioana Triandaf is an applied mathematician specialized in dynamical systems and numerical methods for partial differential equations. She has been modeling swarms since 2004. She is the recipient of the NRL 2005 Alan Berman award for her work on swarming. Since 2017 she collaborated with roboticists in implementing swarming motion on robotic systems. Currently Dr Triandaf is focusing on analyzing and testing swarm disruption methods and metrics.

Title: Optimizing heuristic search algorithms using neural networks

Abstract:

On the opposite side of the uninformed search algorithms, performing a systematic search, heuristic search algorithms are based on multiple rules leading them to estimate, in a predictive way, the minimal cost of the path from the current state to the goal. In this sense, the A* algorithm is an example of heuristics-based algorithms that can guarantee to find a least-cost path to a goal state if this algorithm is using an “admissible heuristic”. A heuristic is said to be “admissible” if it never overestimates the real path cost from the current state to the goal. Furthermore, if the condition h(x) ≤ d(x, y) + h(y) is satisfied by the heuristic h (d denotes that edge length), for every edge (x,y), then his called consistent. And with consistent heuristics, finding an optimal path without processing any node more than once is guaranteed. The main idea consists of developing a Neural Network that can optimize those heuristics to further refine the A* algorithm results. Towards achieving that goal we must find the best synaptic coefficients, and for that reason, a learning phase will be needed during which the network parameters are adjusted until the best admissible and consistent heuristic is obtained, dominating any other heuristic (h1 dominates h2 if for every node n (state), h1(n)>h2(n) ). During this learning phase, and as inputs, the neural network will have some representative examples in the form of pairs of several problems and heuristics ({P1,h1};{P2,h2}...{Pn,hn}), to finally be able to calculate the best heuristic regardless of the inputs.

Biography:

Ouardi Amine, 28 years old, head of Architecture perimeter, ELIS project, Capgemini; Artificial Intelligence Ph.D. student at ENSET Mohammedia, working on optimizing heuristics search algorithms using Neural Networks. Had a Master's degree in Imaging and Business Intelligence, with a graduation project on the Internet Of Things, including QR codes and NFC technology. Got a fundamental license degree in Mathematics and Computer Science, with a final project related to Genetic Algorithms: studying optimal solutions for the Travelling Salesman Problem.

Title: Some phenomenological considerations in computational modeling of analogy

Abstract:

There are currently several models of the analogy process that have been implemented by computer programs. Analogy is a process that we use daily in basic thinking and learning tasks, and is manifested through memories, expectations, and, in general, associations by similarities and differences. Using analogies is something as spontaneous and familiar as implicitly effective in our continuous and our everyday thinking processes. However, aiming to have a complete description of analogy process as humans use, it is not an easy task. In this sense, the simulation of analogy process by computational programs, in general, weakly accounts for its complexity. Phenomenology can help us to unveil the presuppositions analogy operates with, by identifying and discovering the most original forms of objects constitution regarding their genetic origin in the passive (or precognitive) and active synthesis. Thus, genetic phenomenology distinguishes between active genesis, on the one hand, and passive genesis, on the other hand. In the former, the subject participates actively in the constitution of objects ranging from tools and utensils of daily living to systems of thought, artistic creations, mathematical theorems, or scientific theories. Every active genesis presupposes in the subject a kind of passivity that affects it beforehand. This “passivity” does not mean inactivity, but it rather refers to the fact of being involuntarily affected by a variety of habits, for example, dispositions, thinking patterns, motivations, emotions, memories, traditions, and paradigms. Phenomenology of association considers the constitutive role of homogeneity and heterogeneity syntheses which would be the analogical channels or basic elements of thinking generation by analogy.

Biography:

Jose Eduardo García Mendiola is completing his Ph.D. at the Institute of Philosophical Research at the National Autonomous University of Mexico. His research topic is about computational modeling of analogical thinking and the ones related to the philosophy of mind, logic, and artificial intelligence. He has a master's degree in philosophy and a bachelor's in philosophy and mathematics as well. He is a professor at the University of Colima, Mexico.

Title: Consolidated Artificial Intelligence Method for A real-Time Energy Management

Abstract:

Social distancing restrictions due to covid-19 pandemic calls for automation of homes/offices to avoid clustering. Remotely control of appliances upon necessary authorization, therefore, becomes expeditious in observing social distancing. This paper proposes a tinyML-like concept that addresses real-time constraints in the control of electronics appliances especially in this pandemic era and equally serves as an energy conservation scheme.  Existing automated home energy management methods was holistically investigated, and a real-time monitoring and control system called Home Energy Management System (HEMS); developed using Smart Phone, Message Queuing Telemetry Transport (MQTT), and ESP32 microcontroller proposed. Considerations on coverage, security, and appropriate place of implementation were incorporated in the design. This efficient and smart App is proposed for a low-powered and low memory storage device like mobile devices due to their relevance in real-time monitoring of individual load profile and mobility advantage. This semi-automated HEMS was based on edge computing to reduce high latency often associated with cloud computing. The motivation behind this system is the need to monitor and remotely control electronics appliances including the smart meter itself irrespective of the location. Partial automation was implemented to allow the human user a reasonable control. We tested this system in the Naresuan University, School of Renewable Energy & Smart Grid Technology’s smart office. Its energy management capabilities, response time to command, and processing speed proved its promptness and energy-saving potencies. A comparative analysis carried out between the energy consumption of a manually operated office and a smart office using the proposed HEMS showed the latter saved about 24% energy.

Biography:

Alexander N. Ndife is a Research Assistant and Doctoral candidate in Smart Grid Technology at Naresuan University, Thailand. He obtained a bachelor’s degree in Electrical/Electronic Engineering, Anambra State University, Nigeria in 2008 with a specialty in telecommunications and subsequently a master’s degree in Electronics and Computer (Communications) Engineering from Nnamdi Azikiwe University, Nigeria in 2014. His research interests include Wireless Networks, Artificial Intelligent Systems, Deep Learning, Cyber Networks, Image, and Digital Signal Processing. He is a registered engineer in Nigeria and a member of various engineering organizations including the Nigerian Society of Engineers (NSE), IEEE, IAENG and belongs to the Society of Wireless Networks.

Title: Understanding social media users' reactions to artificial intelligence (AI) in advertising: A qualitative study on leisure services

Abstract:

The advancement of technology, such as artificial intelligence (AI), enables new and efficient ways for customers to bring solutions to their personalized preferences in the leisure industry. AI is associated with almost all products or services including leisure, such as travel, vacation, and entertainment. AI can reason with people's social media comments and posts and may reveal their needs, preferences, trends and values. AI has opened up a great opportunity for advertisers to send personalized messages to consumers. While AI is having a major influence in leisure services, consumer reactions to AI advertising have been understudied in the literature. To enhance understanding about the effect of AI advertising messages, this qualitative study included in-depth interviews that investigates consumer reactions to AI-based advertisement messages among social media users. A total of 23 interviews were conducted with social media users aged 18 and over, using purposive sampling method. When determining the sample size in this study, researchers applied saturation as a guiding principle during data collection (Glaser & Strauss, 2017). Qualitative data analysis procedures, as suggested by Braun and Clarke (2006), were applied to this study. Findings indicate that attractiveness, spending time, entertainment, and compatibility in customization are significant factors in determining customer satisfaction and purchase intention. Furthermore, current study participants indicate that price, quality, payment opportunities provided by AI advertisements are highly significant. The results of the current study suggest that artificial intelligence based advertising message was a significant variable, especially when time scarcity, personalization and indecision were high. Knowing the factors that explain consumer reactions to personalized promotional posts in leisure services, such as travel, vacation and entertainment can help in planning strategies for gaining effective results among millennials. Based on findings, managerial implication for customized AI advertisements are formulated.

Biography:

Metin Argan is a Professor of Marketing at the Eskisehir Technical University in Turkey. His research interests primarily relate to sports marketing, fan behavior, leisure, and health care. He also published books, such as sports marketing, entertainment marketing, sport sponsorship management, and recreation management. He has also contributed chapters to several academic books such as sports marketing, and customer relationship management in retailing. He has published refereed articles and books and gave numerous presentations at conferences around the globe. He is a reviewer for many academic journals. Currently, he is investigating e-sports, artificial reality, augmented reality in sports and leisure.