Review of swarm intelligence-based feature selection methods

An improved ant colony algorithm is proposed for robot path planning under a static environment.Grid method is used to establish workspace model of the robot.Furthermore,the strategy of backspace from traps and the meeting strategy were applied to path planning of mobile robot,so it avoided path-locked situation as well as improved the efficiency of planning optimal path.The simulation results show that the performance of path planning can be obviously improved by the proposed ant colony algorithm,and the algorithm is very simple and efficient.

An ImproVed Ant Colony Algorithm for Robot Path Planning

Educational Data Mining plays a critical role in advancing the learning environment by contributing state-of-the-art methods, techniques, and applications. The recent development provides valuable tools for understanding the student learning environment by exploring and utilizing educational data using machine learning and data mining techniques. Modern academic institutions operate in a highly competitive and complex environment. Analyzing performance, providing high-quality education, strategies for evaluating the students’ performance, and future actions are among the prevailing challenges universities face. Student intervention plans must be implemented in these universities to overcome problems experienced by the students during their studies. In this systematic review, the relevant EDM literature related to identifying student dropouts and students at risk from 2009 to 2021 is reviewed. The review results indicated that various Machine Learning (ML) techniques are used to understand and overcome the underlying challenges; predicting students at risk and students drop out prediction. Moreover, most studies use two types of datasets: data from student colleges/university databases and online learning platforms. ML methods were confirmed to play essential roles in predicting students at risk and dropout rates, thus improving the students’ performance.

A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques

State-of-the-Art Review on Advancements of Data Mining in Structural Health Monitoring

Feature selection, which plays an important role in high-dimensional data analysis, is drawing increasing attention recently. Finding the most relevant and important features for classifications are one of the most important tasks of data mining and machine learning, since all of the datasets have irrelevant features that affect accuracy rate and slow down the classifier. Feature selection is an optimization process, which improves the accuracy rate of data classification and reduces the number of selected features. Applying too many features both requires a large memory capacity and leads to a slow execution speed. Feature selection algorithms are often responsible to decide which features should be selected to be used during a classification algorithm. Traditional algorithms seemed to be inefficient due to the complexity of dimensions of the problem, thus evolutionary algorithms were used to improve the problem solving process. The algorithm proposed in this paper, chaotic cuckoo optimization algorithm with levy flight, disruption operator and opposition-based learning (CCOALFDO), is applied to select the optimal feature subspace for classification. It reduces the randomization in selecting features and avoids getting stuck in local optimum solutions which lead to a more interesting feature subset. Extensive experiments are conducted on 20 high-dimensional datasets to demonstrate the effectiveness and efficiency of the proposed method. The results showed the superiority of the proposed method to state-of-the-art methods in terms of classification accuracy rate. In addition, they prove the ability of the CCOALFDO in selecting the most relevant features for classification tasks. Thus, it is a reasonable solution in handling noise and avoiding serious negative impacts on the classification accuracy rate in real world datasets.

Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator

Feature subset selection is considered to be a significant task in data mining. There is a need to develop optimal solutions with higher order of computational efficiency. In this paper, we reviewed the problems encountered during the process of feature selection and how swarm intelligence has been used for extraction of optimal set of features. It also gives a concise overview of various swarm intelligence algorithms like particle swarm optimization, ant colony optimization, bacteria foraging algorithms, bees algorithm, BAT algorithms and the various hybrid approaches that have been discovered using these approaches.

Swarm Intelligence for Feature Selection: A Review of Literature and Reflection on Future Challenges

As we know that Internet, a rebellion innovation has transformed everything, Internet of Things has made a hope for excellent future of Internet with Machine-Machine (M2M) type of communication This review paper has also shown that it is possible and affordable to construct these smart systems based on Internet of Things (IoT) IoT delivers exceptional advancement in the healthcare domain This paper explores the roles of IoT that revolutionize the healthcare domain by providing enormous healthcare benefits to the mankind by offering affordable and practical healthcare solutions The main focus of this paper is to discuss the role of IoT in smart hospitals and its significance to deal with pandemics is also highlighted For serving the community-specific needs during spread of pandemic, various smart devices can be utilized that can provide varied functionalities including proper monitoring of high-risk patients, tracking their bio-metric measurements and capturing real-time data We have also studied various blueprints which can sense unforeseen happenings using numerous sensors and reveal the facts accumulated on LED display Observational outcomes have shown good agreement with the hypothetical statements © 2021 Institute of Physics Publishing All rights reserved

Application of IoT in Current Pandemic of COVID-19

Data Mining has adopted by many areas like education, telecommunication, retail management etc. to resolve their business problems. Due to features likes classification, clustering and association rule mining, it becomes imperative. In this paper, for building predictive classification models algorithms like Naive-Bayes, Decision Tree, Random-Forest, JRip, and ZeroR are implemented on student academic performance dataset. In our implementation results, we found that school, as well as study-time, also affect the final student grade. Classification algorithms like One Rule, Joint Reserve Intelligence Program and Decision Tree have more than 80.00 % accuracy for predicting student result, and they perform equally well.

Student’s Academic Performance Prediction in Academic using Data Mining Techniques

Healthcare industry is evolving at a rapid pace across the world. Substantial amount of heterogeneous data is generated by healthcare industry. It is crucial for healthcare industries to attain, accumulate and mine the data in an effective manner. Thus, data mining can provide enormous opportunities for diagnosis, prediction and essential treatment. Data mining plays imperative role in the medical domain that facilitates the systems to analyze the massive data, so that finest practices can be applied for further research and evaluating the patients' reports. This paper explores various applications that employ Swarm Intelligence with data mining in healthcare in terms of methods and results obtained. Swarm Intelligence algorithms have been used for prognosis of major diseases like cancer, heart diseases, tumors, and cardiology. The Swarm Intelligence approaches have been applied in the areas of disease diagnosis and treatment.

Swarm intelligence and data mining: a review of literature and applications in healthcare

Ant colony optimization (ACO) is a meta-heuristic that is inspired by real ants that are capable of exploring shortest paths, which inspires researchers to apply it for solving numerous optimization problems Outstanding and acknowledged applications are derived from biologically activated algorithms like ACO that are established from artificial swarm intelligence which in turn is motivated by the amalgamated behavior of social insects ACO is influenced by natural ants system, their behavior, team planning and organization, their integration for seeking and finding the optimal solution and also to preserve data of each ant Currently, ACO has appeared as a popular meta-heuristic technique for finding the solution of conjunctional optimization problems that is beneficial for finding shortest paths via construction graphs This paper highlights the behavior of ants and various ACO algorithms (their variants as well as hybrid approaches) that are used successfully for performing feature selection, applications of ACO and current trends The fundamental ideas of ant colony optimization is reviewed including its biological background and application areas This paper portrays how current literature utilizes the ACO approach for performing feature selection By analyzing the literature, it can be concluded that ACO is a suitable approach for feature selection

Nandini Nayar

Papers

Swarm Intelligence for Feature Selection: A Review of Literature and Reflection on Future Challenges

Application of IoT in Current Pandemic of COVID-19

Student’s Academic Performance Prediction in Academic using Data Mining Techniques

Swarm intelligence and data mining: a review of literature and applications in healthcare

Ant Colony Optimization: A Review of Literature and Application in Feature Selection