Blockchain Papers

Marko Mihajlovic, Luka Jovanović, Miloš Antonijević, Svetlana Andjelic · 7 authors

No abstract is available for this record.

A. Manju Priya, T. Biju Daniel, V. Padmapriya, S. Esther Praveena

Swarm intelligence, inspired by the collective behavior observed in social organisms, has emerged as a powerful paradigm in both natural and artificial systems. The concept of a swarm refers to a large group of simple agents that interact locally with one another and their environment, giving rise to complex and intelligent behavior at the group level. Swarm intelligence, on the other hand, represents the ability of a swarm to self-organize, adapt, and solve complex problems without central control. In nature, swarms of social insects such as bees, ants, termites, and birds exhibit remarkable abilities in foraging, navigation, resource allocation, and defense. These organisms demonstrate how the interactions of simple individuals can lead to efficient and robust solutions to various challenges faced in their environments. In artificial systems, researchers have successfully translated the principles of swarm intelligence into algorithms and techniques for optimization, decision-making, and problem-solving. Popular swarm intelligence algorithms, such as Ant Colony Optimization, Particle Swarm Optimization, and Artificial Bee Colony, have shown great promise in tackling complex optimization and search tasks. This paper provides an overview of the fundamental concepts of swarm intelligence and explores the similarities and differences between natural and artificial swarms. It delves into the principles of self-organization, decentralized decision-making, and adaptation that underpin swarm intelligence, allowing these systems to cope with dynamic and uncertain environments. Furthermore, the paper examines the application domains of swarm intelligence, ranging from robotics and autonomous systems to data clustering, image processing, and network routing. The potential of swarm robotics in solving real-world challenges, such as environmental monitoring, disaster response, and precision agriculture, is also explored. Swarm intelligence presents a compelling avenue for understanding and harnessing emergent collective behavior in both biological and computational contexts. The interplay of simplicity, local interactions, and adaptation enables swarms to tackle complex problems efficiently, making them a valuable source of inspiration for the design of intelligent systems in various fields. The study of swarm intelligence continues to advance, offering exciting possibilities for creating adaptive, robust, and scalable solutions in the ever-evolving landscape of artificial intelligence and beyond.

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.

Ekkarat Adsawinnawanawa, Narongdech Keeratipranon

Monte Carlo Algorithm is one of the various algorithms of Reinforcement Learning. It is used with problems that have finite states because of the memory problem. It must remember all the experiences to learn and make a decision. If the agent faces an unseen state, The Agent cannot use the experience for the decision to take the action. With these problems, we proposed the algorithm named The Sharing of Similar Knowledge on Monte Carlo Algorithm (SSKMC) to help Monte Carlo conducted with infinite states and leverage the old experience to decide the action when the agent faces a new experience (unseen state). In this paper, we tested the proposed algorithm with the Cryptocurrency Trading problem (Bitcoin) and compared the testing result of the proposed algorithm to Deep Reinforcement Learning (DRL). By the testing result of The Proposed algorithm makes net worth growth more than the DRL method by 1.55%.

Mohamed Salb, Miodrag Živković, Nebojša Bačanin, Amit Chhabra · 5 authors

No abstract is available for this record.

Dio Satyaloka, Stacyana Giamiko, Akik Hidayat

The emergence of Cryptocurrency has long foreshadowed a more accessible exchange market. Cryptocurrency is easy to use and trade, and this ease of access into the market brought newcomers into the Crypto-trading scene. This surge of inexperienced newcomers causes market instability and major loss amongst themselves. AI models, algorithms, and systems have been long used as an important aspect of prediction. However, the use of AI systems is complex. AI tools and systems often use complicated mathematical formulas and are not easily understood. Amongst these AI systems, Fuzzy Rule-Based Systems (FRBSs) has one of the most easily understood displays. With accuracy that rivals of other less-understood methods, such as Neural Network, FRBSs present us a choice that is easily used by users while keeping the interface as basic and simple as possible. This paper aims to study the use of FRBSs using SK-MOEFS (SciKit-Multi Objective Evolutionary Fuzzy System) Python Library in predicting a bull signal or a bear signal in the Cryptocurrency market while still preserving FRBSs user-friendly nature. The fuzzy sets are partitioned as Very Low, Low, Medium, High, and Very High. Then the resulting classification are used to signal whether a Cryptocurrency is bearish or bullish on the current day. The parameter used on the system yields an undesirable result of 53% accuracy with 25 Total Rule Length, however still producing the desired ease-of-use nature of FRBSs.

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.

Komalpreet Kaur, Yogesh Kumar

Swarm intelligence is the discipline deals with artificial and natural systems that consists various individuals coordinated using self-organization and decentralized control. It consists of simple autonomous agents that come as emergent collective intelligence. The commands from global plan or leader are not followed by autonomous agent. This type of systems has been seen in various domains that makes swarm intelligence as a multidisciplinary character. Due to its popularity, various researchers have started working on it even in computing tasks but still they are unable to give a good survey on various swarm intelligence algorithms and use of it in computing work. Most of the people are unaware about the newly most effective invented swarm intelligence algorithms. In this paper we have given a comprehensive review on various swarm intelligence algorithms that prove to be efficient in multiple fields. The main focus is given to Bat algorithm, Firefly algorithm, Lion optimization algorithm, Chicken swarm optimization algorithm, Social Spider Algorithm and Spider Moneky optimization algorithm. Another thing covered in this paper is the comparative research on use of Swarm intelligence algorithm in Computing work. Cloud computing is the application that delivered as services through Internet and data centers software and hardware. We have covered the research work done by various researchers in cloud computing, Fog computing and Edge computing using swarm intelligence. The motive of this is to show the improvement in work comes after the introduction of Swarm intelligence in computing work.

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.

Stuart H. Rubin, Thouraya Bouabana‐Tebibel, Yasmine Hoadjli, Kadaouia Habib · 5 authors

The solution of NP-hard problems requires the use of one or more explicit or implicit heuristics as a practical measure. Quantum computers promise to make this practical for O (2n) problems or less, but have yet to deliver a solution to a single NP-hard problem. The question addressed by this paper is whether domain transference and reuse of problem-solving knowledge can be mediated through the reuse of heuristics, and, if so, the extent to which such transference may occur in the solution of NP-hard problems. Neural networks have zero domain transference on account of their inability to represent modus ponens. Similarly, CBR, deep learning, EP, GAs, SVMs, the predicate calculus, learning via conventional expert systems, and all other machine learning technologies are unable to theoretically or practically mediate domain transference because they don't respect randomization as the core underpinning technology. The paper offers a constructive proof of the unbounded density of knowledge in support of the Semantic Randomization Theorem (SRT). It details this result and its potential impact on the machine learning community.

Paolo Di Barba

No abstract is available for this record.

Gary B. Lamont

Abstract : Embedding desired behaviors in autonomous vehicles is a difficult problem at best and in general probably impossible to completely resolve in complex dynamic environments. Future technology demands the deployment of small autonomous vehicles or agents with large-scale decentralized swarming capabilities and associated behaviors. Various techniques inspired by biological self-organized systems as found in forging insects and flocking birds, revolve around control approaches that have simple localized rule sets that generate some of the desired emergent behaviors. To computationally develop such a system,an underlying organizational structure or framework is required to control agent rule execution. Thus, autonomous self-organization features are identified and coalesced into an entangled-hierarchical framework. The use of this self-organizing multi-objective evolutionary algorithmic approach dynamically determines the proper weighting and control parameters providing highly dynamic swarming behavior. The system is extensively evaluated with swarms of heterogeneous vehicles in a distributed simulation system with animated graphics. Statistical measurements and observations indicate that bio-inspired techniques integrated with an entangled self-organizing framework can provide desired dynamic swarming behaviors in complex dynamic environments.

Stefan Janson, Daniel Merkle, Martin Middendorf

Purpose The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are connected by a network. The approach is applied to a particle swarm optimization (PSO) algorithm with multiple sub‐swarms. PSO is a nature inspired metaheuristic where a swarm of particles searches for an optimum of a function. A multiple sub‐swarms PSO can be used for example in applications where more than one optimum has to be found. Design/methodology/approach In the studied scenario the particles of the PSO algorithm correspond to data packets that are sent through the network of the computing system. Each data packet contains among other information the position of the corresponding particle in the search space and its sub‐swarm number. In the proposed decentralized PSO algorithm the application specific tasks, i.e. the function evaluations, are done by the autonomous components of the system. The more general tasks, like the dynamic clustering of data packets, are done by the routers of the network. Findings Simulation experiments show that the decentralized PSO algorithm can successfully find a set of minimum values for the used test functions. It was also shown that the PSO algorithm works well for different type of networks, like scale‐free network and ring like networks. Originality/value The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self‐organization and have no central control.

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.