Asian Journal of Research in Computer Science

The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

The Internet of Things (IoT) is a paradigm shift that enables billions of devices to connect to the Internet. The IoT's diverse application domains, including smart cities, smart homes, and e-health, have created new challenges, chief among them security threats. To accommodate the current networking model, traditional security measures such as firewalls and Intrusion Detection Systems (IDS) must be modified. Additionally, the Internet of Things and Cloud Computing complement one another, frequently used interchangeably when discussing technical services and collaborating to provide a more comprehensive IoT service. In this review, we focus on recent Machine Learning (ML) and Deep Learning (DL) algorithms proposed in IoT security, which can be used to address various security issues. This paper systematically reviews the architecture of IoT applications, the security aspect of IoT, service models of cloud computing, and cloud deployment models. Finally, we discuss the latest ML and DL strategies for solving various security issues in IoT networks.

There has been a significant attempt to derive supervised learning models for training Spiking Neural Networks (SNN), which is the third and most recent generation of Artificial Neural Network (ANN). Supervised SNN learning models are considered more biologically plausible and thus exploits better the computational efficiency of biological neurons and also, are less computationally expensive than second generation ANN. SNN models have also produced competitive performance in most tasks when compared to second generation ANNs. These advantages, coupled with the difficulty in adopting the well established learning models for second generation networks to train SNN due to the difference in information coding led to the recent introduction of supervised learning models for training SNN.

However, lack of comprehensive source of literature detailing strides made in this area, and the challenges and prospects of SNN serves as a hindrance to further exploration and application of SNN models. A comprehensive review of supervised learning methods in SNN is presented in this paper in which some widely used SNN neural models, learning models and their basic concepts, areas of applications, limitations, prospects and future research directions are discussed. The main contribution of this paper is that it presents and discusses trends in supervised learning in SNN
with the aim of providing a reference point for those desiring further knowledge and application of SNN methods.

Recently, computer networks faced a big challenge, which is that various malicious attacks are growing daily. Intrusion detection is one of the leading research problems in network and computer security. This paper investigates and presents Deep Learning (DL) techniques for improving the Intrusion Detection System (IDS). Moreover, it provides a detailed comparison with evaluating performance, deep learning algorithms for detecting attacks, feature learning, and datasets used to identify the advantages of employing in enhancing network intrusion detection.

Multi-label classification is the process of specifying more than one class label for each instance. The high-dimensional data in various multi-label classification tasks have a direct impact on reducing the efficiency of traditional multi-label classifiers. To tackle this problem, feature selection is used as an effective approach to retain relevant features and eliminating redundant ones to reduce dimensionality. Multi-label classification has a wide range of real-world applications such as image classification, emotion analysis, text mining and bioinformatics. Moreover, in recent years researchers have focused on applying swarm intelligence methods in selecting prominent features of multi-label data. After reviewing various researches, it seems there are no researches that provide a review of swarm intelligence-based methods for multi-label feature selection. Thus, in this study, a comprehensive review of different swarm intelligence and evolutionary computing methods of feature selection presented for the tasks of multi-label classification. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods and categorize them based on different perspectives. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically. We also introduce benchmarks, evaluation measures and standard datasets to facilitate research in this field. Moreover, we performed some experiments to compare existing works and at the end of this survey, some challenges, issues and open problems of this field are introduced to be considered by researchers in future.