Blockchain Papers

Reza Lotfi, Sara Ghaboulian Zare, Alireza Gharehbaghi, Sima Nazari · 5 authors

No abstract is available for this record.

M. Rajesh, T. Archana Acharya, Hafis Hajiyev, E. Laxmi Lydia · 8 authors

Recently, Internet of Things (IoT) has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications. Wireless sensor network (WSN) is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management, intelligent transportation, home automation, smart buildings, and other such IoT-based application. The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is, for balancing the energy amongst sensor nodes. The clustering and routing procedures assumed that Non-Polynomial (NP) hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems. With this motivation, this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning (BCEHGS-RP) scheme for IoT assisted WSN. The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supported WSN environment. In addition, an effective multihop route planning approach was designed by the use of EHGS technique. The proposed EHGS technique is derived from the concept of Hill Climbing strategy (HCS) and HGS algorithm. Moreover, a fitness function with two parameters namely residual energy (RE) and inter-cluster distance to elect optimal routes. The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes. Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

Dong-Hee Cho, Seung‐Hyun Moon, Yong-Hyuk Kim

Feature selection reduces the dimension of input variables by eliminating irrelevant features. We propose feature selection techniques based on a genetic algorithm, which is a metaheuristic inspired by a natural selection process. We compare two types of feature selection for predicting a stock market index and cryptocurrency price. The first method is a newly devised genetic filter involving a fitness function designed to increase the relevance between the target and the selected features and decrease the redundancy between the selected features. The second method is a genetic wrapper, whereby we can find the better feature subsets related to KOPSI by exploring the solution space more thoroughly. Both genetic feature selection methods improved the predictive performance of various regression functions. Our best model was applied to predict the KOSPI, cryptocurrency price, and their respective trends after COVID-19.

Usman Amjad, Tahseen Ahmed, Humera Tariq, Amir Hussain

Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms.

Oliver Giel

Practical knowledge on the design and application of multi-objective evolutionary algorithms (MOEAs) is available but well-founded theoretical analyses of the runtime are rare. Laumanns, Thiele, Zitzler, Welzel and Deb (2002) have started such an analysis for two simple mutation-based algorithms including SEMO. These algorithms search locally in the neighborhood of their current population by selecting an individual and flipping one randomly chosen bit. Due to its local search operator, SEMO cannot escape from local optima, and, therefore, has no finite expected runtime in general. In this talk, we investigate the runtime of a variant of SEMO whose mutation operator flips each bit independently. It is proven that its expected runtime is O(n^n) for all objective functions f: {0,1}^n -> R^m, and that there are bicriteria problems among the hardest problem for this algorithm. Moreover, for each d between 2 and n, a bicriteria problem with expected runtime Theta(n^d) is presented. This shows that bicriteria problems cover the full range of potential runtimes of this variant of SEMO. For the problem LOTZ (Leading-Ones-Trailing Zeroes), the runtime does not increase substantially if we use the global search operator. Finally, we consider the problem MOCO (Multi-Objective-Counting-Ones). We show that the conjectured bound O((n^2)log n) on the expected runtime is wrong for both variants of SEMO. In fact, MOCO is almost a worst case example for SEMO if we consider the expected runtime; however, the runtime is O((n^2)log n) with high probability. Some ideas from the proof will be presented.