Search Results (6)

Intrusion detection has been a vast-surveyed topic for many decades as network attacks are tremendously growing. This has heightened the need for security in networks as web-based communication systems are advanced nowadays. The proposed work introduces an intelligent semi-supervised intrusion detection system based on different algorithms to classify the network attacks accurately. Initially, the pre-processing is accomplished using null value dropping and standard scaler normalization. After pre-processing, an enhanced Deep Reinforcement Learning (EDRL) model is employed to extract high-level representations and learn complex patterns from data by means of interaction with the environment. The enhancement of deep reinforcement learning is made by associating a deep autoencoder (AE) and an improved flamingo search algorithm (IFSA) to approximate the Q-function and optimal policy selection. After feature representations, a support vector machine (SVM) classifier, which discriminates the input into normal and attack instances, is employed for classification. The presented model is simulated in the Python platform and evaluated using the UNSW-NB15, CICIDS2017, and NSL-KDD datasets. The overall classification accuracy is 99.6%, 99.93%, and 99.42% using UNSW-NB15, CICIDS2017, and NSL-KDD datasets, which is higher than the existing detection frameworks. Full article

Flamingos (Phoenicopteridae) are among the oldest birds worldwide and are loved by people for their bright red feathers. In addition, flamingos are sexually monomorphic birds, and distinguishing between males and females is difficult. The polymerase chain reaction (PCR) is widely used for sex identification. However, the PCR method requires a precise thermal cycler in the laboratory and is time-consuming. Therefore, developing a rapid, sensitive, and accurate method to identify the sex of flamingos is crucial. In this study, we established a sex identification system using a recombinase-aided amplification-Pyrococcus furiosus Argonaute (RAA-PfAgo) technique for greater flamingo (Phoenicopterus roseus). The greater flamingo-RAA-PfAgo system can identify unknown-sex greater flamingos in less than 1 h and can be visualized using a fluorescent detector or blue light. The results showed that optimal RAA-PfAgo conditions could detect 0.6 ng of genomic DNA and effectively differentiate between males and females. Random sample evaluations revealed that the system had a 100% coincidence rate compared with conventional PCR. In conclusion, this study provides a sensitive, specific, and accurate reference method for greater flamingo sexing. Full article

To overcome the limitations of the Flamingo Search Algorithm (FSA), such as a tendency to converge on local optima and improve solution accuracy, we present an improved algorithm known as the Multi-Strategy Improved Flamingo Search Algorithm (IFSA). The IFSA utilizes a cube chaotic mapping strategy to generate initial populations, which enhances the quality of the initial solution set. Moreover, the information feedback model strategy is improved to dynamically adjust the model based on the current fitness value, which enhances the information exchange between populations and the search capability of the algorithm itself. In addition, we introduce the Random Opposition Learning and Elite Position Greedy Selection strategies to constantly retain superior individuals while also reducing the probability of the algorithm falling into a local optimum, thereby further enhancing the convergence of the algorithm. We evaluate the performance of the IFSA using 23 benchmark functions and verify its optimization using the Wilcoxon rank-sum test. The compared experiment results indicate that the proposed IFSA can obtain higher convergence accuracy and better exploration abilities. It also provides a new optimization algorithm for solving complex optimization problems. Full article

Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity. Full article

Managing sustainable rural tourism requires a strategic transformation adapted to local conditions, the complexity of rural institutions, and the dynamics of future changes. In addition, it must be inclusive. This paper presents transformation pathways toward sustainable rural tourism management in developing countries. The general objective is to develop sustainable development strategies in the context of rural tourism. The specific objectives are to develop policy pathways and the best scenarios in this context. The study was conducted in the Kedung Ombo area in Central Java, Indonesia: a representative area involving several districts and other public organizations as stakeholders. Data analysis was performed using the MULTIPOL method. The results show that an integrated development policy that considers the interests of all stakeholders, the potential of rural resources, the infrastructure, and human resources capacity would be the optimal policy. Priority programs to be implemented are infrastructure development, strengthening private investment, strengthening governance, developing amenities, and developing information and communication technology. Furthermore, the “flight of the flamingos” and “leapfrogging” scenarios can be considered to achieve future tourism growth goals and objectives. This study is an essential resource for authorities in determining rural tourism development policies in the research location and can be applied in other areas with similar characteristics. Full article

Geographical, cultural, and socio-economic factors create a different lifestyle. Accordingly, the aim of this research was to identify the differences in anthropometric and fitness characteristics among adolescents living in Montenegro and Kosovo in order to optimize the physical fitness as a consequence of the proactive behavior. Anthropometric characteristics and fitness level measures were assessed by Eurofit testing battery among 600 Kosovar and 600 Montenegrin students, equally distributed by gender. Differences between countries, gender, and age group (13, 14 and 15 years) were assessed with multivariate and univariate analysis of variance. The results showed that Montenegrin students are taller, heavier, and achieve better results in the sit-ups and 20-m endurance shuttle-run tests. Kosovar students achieve better results in the standing broad jump and 10 × 5 m shuttle-run tests. Males achieve significantly better results in all tests, excluding the sit-and-reach test. Statistically significant differences among age groups were found for all variables, regardless gender and country (except for the flamingo and sit-and reach tests). This study is of importance to physical education teachers and curriculum authors to optimize students’ proactive behavior based on the identified anthropometric and physical fitness differences between the two countries, age groups, and genders. Full article