Multi-label feature selection using density-based graph clustering and ant colony optimization Zana Azeez Kakarash, Farhad Mardukhia, Parham Moradi Journal of Computational Design and Engineering, 2023 Multi-label learning is a machine learning subclass that aims to assign more than one label simultaneously for each instance. Many real-world tasks include high-dimensional data which reduces the performance of machine learning methods. To solve this issue, a filter and multi-label feature selection is proposed in this paper. The main idea of the proposed method is to choose highly relevant and non-redundant features with the lowest information loss. The proposed method first uses a novel graph-based density peaks clustering to group similar features to reach this goal. It then uses the ant colony optimization search process to rank features based on their relevancy to a set of labels and also their redundancy with the other features. A graph first represents the feature space, and then a novel density peaks clustering is used to group similar features. Then, the ants are searched through the graph to select a set of non-similar features by remaining in the clusters with a low probability and jumping among the clusters with a high probability. Moreover, in this paper, to evaluate the solutions found by the ants, a novel criterion based on mutual information was used to assign a high pheromone value to highly relevant and non-redundant features. Finally, the final features are chosen based on their pheromone values. The results of experiments on a set of real-world datasets show the superiority of the proposed method over a set of baseline and state-of-the-art methods.
A Temporal and Social Network-based Recommender using Graph Clustering Nawroz Ahmed, Karwan Hamakarim, Zana Kakarash Passer Journal of Basic and Applied Sciences, 2022 Recommendation Systems (RSs) have significant applications in many industrial systems. The duty of a recommender algorithm is to operate available data (users/items contextual data and rating (or purchase) the consumption history for items), as well as to provide a recommendation list for any target user. The recommended items should be selected so that the target user is compelled to give them positive reviews. In this manuscript, we propose a novel of RS algorithm that makes advantage of user-user trust relationships, rating histories, and their frequency of occurrence. We also provide a brand new overlapping community detection algorithm. The information about the users’ community structure is used to handle the cold-start and sparsity problems. We compare the performance of the proposed RS algorithm with a number of state-of-the-art algorithms on the extended Epinions dataset, which has both information on trust relations and the timing of the ratings. Numerical simulations reveal the superiority of the proposed algorithm over others. We also investigate how the algorithms perform when only cold-start users and items are considered. As a cold-start user (item) we consider those that have made (received) less than five ratings. The experiments show significant outperformance of the proposed algorithm over others, which is mainly due to the use of information on overlapping community structures between users.
Increasing the Efficiency of a Control System for Detecting the Type and Amount of Oil Product Passing through Pipelines Based on Gamma-Ray Attenuation, Time Domain Feature Extraction, and Artificial Neural Networks Abdulilah Mohammad Mayet, Seyed Mehdi Alizadeh, Zana Azeez Kakarash, Ali Awadh Al-Qahtani, Abdullah K. Alanazi, John William Grimaldo Guerrero, Hala H. Alhashimi, Ehsan Eftekhari-Zadeh Polymers, 2022 Instantaneously determining the type and amount of oil product passing through pipelines is one of the most critical operations in the oil, polymer and petrochemical industries. In this research, a detection system is proposed in order to monitor oil pipelines. The system uses a dual-energy gamma source of americium-241 and barium-133, a test pipe, and a NaI detector. This structure is implemented in the Monte Carlo N Particle (MCNP) code. It should be noted that the results of this simulation have been validated with a laboratory structure. In the test pipe, four oil products—ethylene glycol, crude oil, gasoil, and gasoline—were simulated two by two at various volume percentages. After receiving the signal from the detector, the feature extraction operation was started in order to provide suitable inputs for training the neural network. Four time characteristics—variance, fourth order moment, skewness, and kurtosis—were extracted from the received signal and used as the inputs of four Radial Basis Function (RBF) neural networks. The implemented neural networks were able to predict the volume ratio of each product with great accuracy. High accuracy, low cost in implementing the proposed system, and lower computational cost than previous detection methods are among the advantages of this research that increases its applicability in the oil industry. It is worth mentioning that although the presented system in this study is for monitoring of petroleum fluids, it can be easily used for other types of fluids such as polymeric fluids.
Introducing a Precise System for Determining Volume Percentages Independent of Scale Thickness and Type of Flow Regime Abdulilah Mohammad Mayet, Seyed Mehdi Alizadeh, Zana Azeez Kakarash, Ali Awadh Al-Qahtani, Abdullah K. Alanazi, Hala H. Alhashimi, Ehsan Eftekhari-Zadeh, Ehsan Nazemi Mathematics, 2022 When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent.
Time Series Forecasting Based on Support Vector Machine Using Particle Swarm Optimization Zana Azeez Kakarash, Hawkar Saeed Ezat, Shokhan Ali Omar, Nawroz Fadhil Ahmed International Journal of Computing, 2022 In recent years, due to the non-linear nature, complexity, and irregularity of time series, especially in energy consumption and climate, studying this field has become very important. Therefore, this study aims to provide a high accuracy and efficiency hybrid approach to time series forecasting. The proposed model is called EDFPSO-SVR (Empirical Mode Decomposition- Discrete Wavelet Transform- Feature selection with Particle Swarm Optimization-Support Vector Regression). In the proposed hybrid approach, the first step is to decompose the signal into the Intrinsic Mode Functions (IMF) component using the Empirical Mode Decomposition (EMD) algorithm. In the second step, each component is transformed into subsequences of approximation properties and details by converting the Wavelets. In the third step, the best feature is extracted by the PSO algorithm. The purpose of using the PSO algorithm is feature extraction and error minimization of the proposed approach. The fourth step, using time vector regression, has dealt with time series forecasting. Four data sets in two different fields have been used to evaluate the proposed method. The two datasets are electric load of England and Poland, and the other two datasets are related to the temperature of Australia and Belgium. Evaluation criteria include MSE, RMSE, MAPE, and MAE. The evaluation results of the proposed method with other Principal component analysis (PCA) feature extraction algorithms, and comparisons with methods and studies in this field, indicate the proper performance of the proposed approach.
New Topology Control base on Ant Colony Algorithm in Optimization of Wireless Sensor Network Zana Azeez Kakarash, Sarkhel H.Taher Karim, Nawroz Fadhel Ahmed, Govar Abubakr Omar Passer Journal of Basic and Applied Sciences, 2021 Wireless sensor networks (WSNs) have found great appeal and popularity among researchers, especially in the field of monitoring and surveillance tasks. However, it has become a challenging issue due to the need to balance different optimization criteria such as power consumption, packet loss rate, and network lifetime, and coverage. The novelty of this research discusses the applications, structures, challenges, and issues we face in designing WSNs. And proposed new Topology control mechanisms it will focus more on building a reliable and energy efficient network topology step by step through defining available amount of energy for each node within its cluster, sorting all within header, and selecting an active one (more power header) for signal routing. While sensor cover topology demonstrates network monitoring capability, connection topology should remain as a requirement for the successful delivery of information including queries, data collected, and control messages. How to build an optimized coating topology while remaining efficient and low-cost connection is not well understood and needs further research. Power control and power management are two different types of topology controllers. Also in our study, we examine network lifetime, compared to other schemas time of death of the first node and the last node, and found that network lifetime was increased. Finally, a topology control method for extending network lifetime is presented.
Effective data parallel optimization for quantifying the mathematical model of subsystems in multivariable systems Advanced Mathematical Models and Applications, 2021
A survey of neighborhood construction algorithms for clustering and classifying data points Shahin Pourbahrami, Mohammad Ali Balafar, Leyli Mohammad Khanli, Zana Azeez Kakarash Computer Science Review, 2020 Clustering and classifying are overriding techniques in machine learning. Neighborhood construction as a key step in these techniques has been extensively used for modeling local relationships between data samples, and constructing global structures from local information. The goal of the neighborhood construction process is to improve the quality of individual data point categorizing. Many applications such as detecting social network communities, bundling related edges, solving location, and routing problems all indicate the importance of this problem. This paper presents theoretical and practical studies of state-of-the-art methods in the context of neighborhood construction which is resulted in a coherent and comprehensive survey to analyze these methods. To this end, significant algorithms of neighborhood construction have been proposed to analyze data points which are very useful for the community of clustering and classifying practitioners since showing the advantages and disadvantages of each algorithm. All of them will be described and discussed deeply in different aspects, which help to select an appropriate solution for problems. A taxonomy of these algorithms is presented and their differences and some important applications are explained. Finally, the future challenges concerning the title of the present paper are outlined.
The dimensions of petri nets: Modelling strategies for biological aspects application to E. coli Journal of Theoretical and Applied Information Technology, 2017
Fingerprint biometric identification with geometric moment features Journal of Theoretical and Applied Information Technology, 2017
