Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach

A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration

Some optimization problems are difficult to solve due to a considerable number of local optima, which may result in premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach coupled with a self-tuning fitness function. More specifically, to evaluate the fitness of the produced surrogate functions, we employ Fuzzy Self-Tuning Particle Swarm Optimization, a setting-free version of particle swarm optimization. To assess the performance of the proposed method, we considered a set of benchmark functions characterized by high noise and ruggedness. Moreover, the method is evaluated over different problems’ dimensionalities. The proposed approach reveals its suitability for performing the proposed task. In particular, experimental results confirm its capability to find the global argminimum for all the considered benchmark problems and all the domain dimensions taken into account, thus providing an innovative and promising strategy for dealing with challenging optimization problems. Doi: 10.28991/ESJ-2023-07-04-01 Full Text: PD

A neural networks benchmark for image classification

A través de este documento, el lector puede hacerse hacerse una idea de como ha sido la historia de los métodos usados para clasificación de imagen, tanto con métodos clásicos como con redes neuronales artificiales; todo ello en el contexto de visión para robots. Primero revisaremos los métodos clásicos, pasando por sus restricciones y limitaciones, escogeremos uno y sacaremos diferentes medidas sobre cómo se comporta. Después, exploraremos, brevemente, los tipos de redes neuronales que se utilizan para esta tarea, pasando por el estado del arte y su aportación; también escogeremos una red y mediremos su eficacia. Con todo ello, explicaremos el método empleado para medir y los resultados experimentales obtenidos. Por último discutiremos estos resultados y expondremos nuestras conclusiones, así como posibles lineas futuras de investigación. Through this document, the reader can get an idea of the history of modern and classic methods for the task of image classification, we present a simple image classification task, in the context of robotic vision, and how different neural networks reach to stable solutions. First, we'll review different classic methods, evaluating their constraints and limitations, only to pick one up and benchmark it. Then, briefly, we will explore more modern methods, choose one, and benchmark it. Then, both benchmarks will be compared, and experimental results will be analyzed and explained. We'll conclude with a discussion of the results, pointing out future lines of research

Fuzzy approach for Arabic character recognition

Pattern recognition/classification is increasingly drawing the attention of scientific research because of its important roll in automation and human-machine communication. Even though many models have been introduced to deal with classification, because of the inherited imprecision and ambiguity, these models did not tackle the problem in an efficient way. Traditional models deal only with statistical uncertainty (randomness) but not with the non-statistical uncertainty (vagueness). Fuzzy set theory allows us to better understand imprecision in both of its categories: vagueness and randomness. The incorporation of fuzzy set theory in existing algorithms helped in many cases to improve the performance and increase the efficiency of those algorithms. This thesis will explore fuzzy logic as it pertains to pattern recognition. In order to demonstrate fuzzy logic, the problem of recognizing the Arabic alphabet is discussed. In this problem moments and central moments were used as discriminating features. A fuzzy classifier was designed in a way that incorporated some statistical knowledge of the problem in hand. Performance of this classifier was compared to a Bayesian classifier and a neural network classifier. Performance, evaluation, and advantages and disadvantages of each classifier is reported and discussed

Active aging in place supported by caregiver-centered modular low-cost platform

Aging in place happens when people age in the residence of their choice, usually their homes because is their preference for living as long as possible. This research work is focused on the conceptualization and implementation of a platform to support active aging in place with a particular focus on the caregivers and their requirements to accomplish their tasks with comfort and supervision. An engagement dimension is also a plus provided by the platform since it supports modules to make people react to challenges, stimulating them to be naturally more active. The platform is supported by IoT, using low-cost technology to increment the platform modularly. Is a modular platform capable of responding to specific needs of seniors aging in place and their caregivers, obtaining data regarding the person under supervision, as well as providing conditions for constant and more effective monitoring, through modules and tools that support decision making and tasks realization for active living. The constant monitoring allows knowing the routine of daily activities of the senior. The use of machine learning techniques allows the platform to identify, in real-time, situations of potential risk, allowing to trigger triage processes with the older adult, and consequently trigger the necessary actions so that the caregiver can intervene in useful time.O envelhecimento no local acontece quando as pessoas envelhecem na residência da sua escolha, geralmente nas suas próprias casas porque é a sua preferência para viver o máximo de tempo possível. Este trabalho de investigação foca-se na conceptualização e implementação de uma plataforma de apoio ao envelhecimento ativo no local, com particular enfoque nos cuidadores e nas suas necessidades para cumprir as suas tarefas com conforto e supervisão. Uma dimensão de engajamento também é um diferencial da plataforma, pois esta integra módulos de desafios para fazer as pessoas reagirem aos mesmos, estimulando-as a serem naturalmente mais ativas. A plataforma é suportada por IoT, utilizando tecnologia de baixo custo para incrementar a plataforma de forma modular. É uma plataforma modular capaz de responder às necessidades específicas do envelhecimento dos idosos no local e dos seus cuidadores, obtendo dados relativos à pessoa sob supervisão, bem como fornecendo condições para um acompanhamento constante e mais eficaz, através de módulos e ferramentas que apoiam a tomada de decisões e realização de tarefas para a vida ativa. A monitorização constante permite conhecer a rotina das atividades diárias do idoso, permitindo que, com a utilização de técnicas de machine learning, a plataforma seja capaz de detetar em tempo real situações de risco potencial, permitindo desencadear um processo de triagem junto do idoso, e consequentemente despoletar as ações necessárias para que o prestador de cuidados possa intervir em tempo útil

Applying modern logging for minimize production risks in oil and gas wells

Clearly, everyone who works in oil and gas field knows that logging operations are very important to produce our oil and gas without any risks and undesirable incidents. There are many different purposes to use logging in our well. For instance, one of them is to find out our production point in oil and gas well. After the logging operation, the report paper of logging operation shows us where our resource is located. According to this, we can decide how many meters our well deep. Other reason using log is to determine curing time of cement. After running casing, immediately, cementing engineers come to the field to pump the cement to free space between the casings or casing and wellbore. These are different operations that’s why we use different kind of the log to analyze. Therefore, cement logging also is important. In this thesis, we are going to analyze and observe real cases and results of the logging operations in oil and gas fields. Mainly, in production zones logging operations should be done because undesirable incidents and risks are more than other zones. Currently, we use the most modern logging in our fields to ensure that everything is okay, and we can continue other operations. Production zone which is perforated is more dangerous zone because perforated zones may be banned, and it may cause well lost. As a result, production engineers lost their production zone, company is out of pocket and loss well structure. Learning about logs and logging operations and how to use with them helps to improve technical operations, also we don’t waste time with undesirable accidents, safety factor increases in the field. The most main thing in the oil and gas field is safety. Improving our capability and knowledge about using logs to maximize safety in the platform

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

Evaluating the effectiveness of land-use policies in preventing the risk of coastal flooding

Today, urban environments face several wicked environmental problems. These problems respond to institutional and political approaches instead of technical solutions. Rising sea levels, for instance, put waterfront areas at risk of coastal flooding. To minimize this risk land-use regulations can be effective. Although many strategies have been developed for managing wicked problems, few efforts have focused on evaluating the effectiveness of land-use policies in regulating wicked environmental problems. This study develops a framework for evaluating the effectiveness of land-use policies in preventing sea flood risks. The study area covers the coastal regions of Helsinki and Espoo, and the timeframe of this evaluation expands from 2000 to 2018. Through the developed framework, land-use scenarios are simulated based on specific values, which reflect the effects of a policy set. This framework can be adapted to assess the effectiveness of land-use policies on different land-use conversions. The data used to conduct this research include the CORINE land-use cover dataset and the sea flood risk dataset provided by the Finnish Environment Institute (SYKE), international land-use planning and regulation guidelines, national legislation, and other relevant documents. Furthermore, land-use simulations were generated by GeoSOS-FLUS software. According to the results, fewer vulnerable land-use types are located within the sea flood risk zones in 2018 compared to 2000. This demonstrates positive land-use planning performance in the target areas. This simulation also shows a strong similarity to actual land use in 2018, proving the framework's reliability