Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

An empirical study on the various stock market prediction methods

Investment in the stock market is one of the much-admired investment actions. However, prediction of the stock market has remained a hard task because of the non-linearity exhibited. The non-linearity is due to multiple affecting factors such as global economy, political situations, sector performance, economic numbers, foreign institution investment, domestic institution investment, and so on. A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors. This review provides a detailed analysis of research papers presenting stock market prediction techniques. These techniques are assessed in the time series analysis and sentiment analysis section. A detailed discussion on research gaps and issues is presented. The reviewed articles are analyzed based on the use of prediction techniques, optimization algorithms, feature selection methods, datasets, toolset, evaluation matrices, and input parameters. The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters. In addition, major problems raised in the present techniques are also discussed. This survey will provide researchers with deeper insight into various aspects of current stock market prediction methods

Neuro-Fuzzy Combination for Reactive Mobile Robot Navigation: A Survey

Autonomous navigation of mobile robots is a fruitful research area because of the diversity of methods adopted by artificial intelligence. Recently, several works have generally surveyed the methods adopted to solve the path-planning problem of mobile robots. But in this paper, we focus on methods that combine neuro-fuzzy techniques to solve the reactive navigation problem of mobile robots in a previously unknown environment. Based on information sensed locally by an onboard system, these methods aim to design controllers capable of leading a robot to a target and avoiding obstacles encountered in a workspace. Thus, this study explores the neuro-fuzzy methods that have shown their effectiveness in reactive mobile robot navigation to analyze their architectures and discuss the algorithms and metaheuristics adopted in the learning phase

Maximum Power Point Tracking Algorithm for Advanced Photovoltaic Systems

Photovoltaic (PV) systems are the major nonconventional sources for power generation for present power strategy. The power of PV system has rapid increase because of its unpolluted, less noise and limited maintenance. But whole PV system has two main disadvantages drawbacks, that is, the power generation of it is quite low and the output power is nonlinear, which is influenced by climatic conditions, namely environmental temperature and the solar irradiation. The natural limiting factor is that PV potential in respect of temperature and irradiation has nonlinear output behavior. An automated power tracking method, for example, maximum power point tracking (MPPT), is necessarily applied to improve the power generation of PV systems. The MPPT methods undergo serious challenges when the PV system is under partial shade condition because PV shows several peaks in power. Hence, the exploration method might easily be misguided and might trapped to the local maxima. Therefore, a reasonable exploratory method must be constructed, which has to determine the global maxima for PV of shaded partially. The traditional approaches namely constant voltage tracking (CVT), perturb and observe (P&O), hill climbing (HC), Incremental Conductance (INC), and fractional open circuit voltage (FOCV) methods, indeed some of their improved types, are quite incompetent in tracking the global MPP (GMPP). Traditional techniques and soft computing-based bio-inspired and nature-inspired algorithms applied to MPPT were reviewed to explore the possibility for research while optimizing the PV system with global maximum output power under partially shading conditions. This paper is aimed to review, compare, and analyze almost all the techniques that implemented so far. Further this paper provides adequate details about algorithms that focuses to derive improved MPPT under non-uniform irradiation. Each algorithm got merits and demerits of its own with respect to the converging speed, computing time, complexity of coding, hardware suitability, stability and so on

Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm

Dementia is a brain disease that stays in the seventh position of death rate as per the report of the World Health Organization (WHO). Among the various types of dementia, Alzheimer’s disease has more than 70% of cases of dementia. The objective is to predict dementia disease from the open access series of imaging studies (OASIS) dataset using machine learning techniques. Also, the performance of the machine learning model is analyzed to improve the performance of the model using the cuckoo algorithm. In this paper, feature engineering has been focused and the prediction of dementia has been done using the OASIS dataset with the help of data mining techniques. Feature engineering is followed by prediction using the machine learning model Gaussian naïve Bayes (NB), support vector machine, and linear regression. Also, the best prediction model has been selected and done the validation. The evaluation metrics considered for validating the models are accuracy, precision, recall, and F1-Score and the highest values are 95%, 97%, 95%, and 95%. The Gaussian NB has been given these best results. The accuracy of the machine learning models has been increased by eliminating the factors which affect the performance of the models using the cuckoo algorithm

Computational Intelligence in Modeling Complex Systems and Solving Complex Problems

Editoria

Artificial Intelligence in Civil Engineering

Artificial intelligence is a branch of computer science, involved in the research, design, and application of intelligent computer. Traditional methods for modeling and optimizing complex structure systems require huge amounts of computing resources, and artificial-intelligence-based solutions can often provide valuable alternatives for efficiently solving problems in the civil engineering. This paper summarizes recently developed methods and theories in the developing direction for applications of artificial intelligence in civil engineering, including evolutionary computation, neural networks, fuzzy systems, expert system, reasoning, classification, and learning, as well as others like chaos theory, cuckoo search, firefly algorithm, knowledge-based engineering, and simulated annealing. The main research trends are also pointed out in the end. The paper provides an overview of the advances of artificial intelligence applied in civil engineering

Development and validation of enhanced fuzzy logic controller and boost converter topologies for a single phase grid system

Introduction. Solar photovoltaic system is one of the most essential and demanding renewable energy source in the current days, due to the benefits of high efficiency, reduced cost, no pollution, and environment friendly characteristics. Here, the maximum power point tracking controller has been implemented for obtaining an extreme power from the photovoltaic array. For this purpose, there are different controller and converter strategies have been deployed in the conventional works. It includes perturb and observation, incremental conductance, fuzzy logic systems, and hill climbing, and these techniques intend to extract the high amount of power from the solar systems under different climatic conditions. Still, it limits with the issues like increased design complexity, high cost consumption, high harmonics, and increased time consumption. The goal of this work is to deploy an improved controlling and converter topologies to regulate the output voltage and power fed to the single phase grid systems. The novelty of the work aims to develop an enhanced fuzzy logic controller based maximum power point tracking mechanism with the boost DC-DC converter topology for a single phase grid tied photovoltaic systems. Practical value. Also, the higher order harmonics suppression and unbalanced current elimination are handled by the use of LCL filtering technique, which efficiently reduces the harmonics in the output of inverter voltage and current. Moreover, it helps to obtain the reduced total harmonics distortion value with improved accuracy and efficiency. Results. There are different performance indicators have been evaluated for validating the proposed enhanced fuzzy logic controller–maximum power point tracking controlling technique. Moreover, the obtained results are compared with some of the conventional controlling algorithms for proving the betterment of the proposed work.Вступ. Сонячна фотоелектрична система є одним з найбільш важливих та затребуваних відновлюваних джерел енергії в наші дні завдяки перевагам високої ефективності, низької вартості, відсутності забруднення та екологічно безпечним характеристикам. При цьому було реалізовано контролер стеження за точкою максимальної потужності для отримання екстремальної потужності від фотогальванічної батареї. З цією метою у традиційних роботах використовуються різні стратегії контролерів та перетворювачів. Це включає збурення та спостереження, інкрементну провідність, системи нечіткої логіки та сходження на пагорб, і ці методи призначені для отримання великої кількості енергії із сонячних систем у різних кліматичних умовах. Тим не менш, це обмежується такими проблемами, як підвищена складність конструкції, високі витрати, високі гармоніки та збільшення витрат часу. Метою цієї роботи є розвиток вдосконаленого управління та топології перетворювача для регулювання вихідної напруги та потужності, що подається до однофазних мережевих систем. Новизна роботи спрямована на розробку вдосконаленого механізму відстеження точки максимальної потужності на основі контролера з нечіткою логікою з топологією перетворювача постійного струму, що підвищує, для однофазних фотоелектричних систем, прив'язаних до мережі. Практична цінність. Крім того, придушення гармонік вищих порядків та усунення незбалансованого струму здійснюється за допомогою методу LCL-фільтрації, який ефективно зменшує гармоніку на виході інвертора напруги та струму. Крім того, це допомагає отримати зменшене значення повного гармонійного спотворення з покращеною точністю та ефективністю. Результати. Існують різні показники ефективності, які були оцінені для перевірки запропонованого покращеного контролера нечіткої логіки – методу керування відстеженням точки максимальної потужності. Крім того, отримані результати порівнюються з деякими звичайними алгоритмами контролю для доведення кращості запропонованої роботи