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A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning

Bansal, Malti; Goyal, Apoorva; Choudhary, Apoorva - In: Decision analytics journal 3 (2022), pp. 1-21

Machine learning (ML) is a new-age thriving technology, which facilitates computers to read and interpret from the previously present data automatically. It makes use of multiple algorithms to build models, mathematical in nature, and then makes predictions for the new data using the past data...

Persistent link: https://www.econbiz.de/10013339256

A comparative assessment of machine learning algorithms with the Least Absolute Shrinkage and Selection Operator for breast cancer detection and prediction

Hassan, Md. Mehedi; Hassan, Md. Mahedi; Yasmin, Farhana; … - In: Decision analytics journal 7 (2023), pp. 1-17

cancer detection and prediction. This study presents a comparative assessment of machine learning models for diagnosing …

Persistent link: https://www.econbiz.de/10014497358

A novel fusion Support Vector Machine integrating weak and sphere models for classification challenges with massive data

Pimentel, Jonatha Sousa; Ospina, Raydonal; Ara, Anderson - In: Decision analytics journal 11 (2024), pp. 1-14

The unprecedented growth in data generation has necessitated the adoption of advanced analytical techniques. Support Vector Machine (SVM) is a powerful machine learning tool that has proven invaluable in classifying observations through optimal hyperplane in higher dimensions. Despite their...

Persistent link: https://www.econbiz.de/10015101975

The applications of artificial neural networks, support vector machines, and long-short term memory for stock market prediction

Chhajer, Parshv; Shah, Manan; Kshirsagar, Ameya - In: Decision analytics journal 2 (2022)

opportunity is the application of machine learning and artificial intelligence for stock market prediction. The stock market is …. We discuss the strengths and weaknesses of machine learning for stock market prediction and provide some insight into the … opportunities and threats in applying advanced technologies for stock market prediction. We further study the applications of three …

Persistent link: https://www.econbiz.de/10013163098

A high-frequency trading volume prediction model using neural networks

Xu, Xiaojie; Zhang, Yun - In: Decision analytics journal 7 (2023), pp. 1-8

considered to tackle the prediction problem. Several questions are pursued, including whether the trading volume could be … relatively simple model based upon ten hidden neurons and thirty lags leads to stable and accurate prediction results based on …

Persistent link: https://www.econbiz.de/10014497231

A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises

Mishra, Bhupesh Kumar; Dahal, Keshav; Pervez, Zeeshan; … - In: Decision analytics journal 5 (2022), pp. 1-10

In a disaster scenario, relief items distribution is required as early as possible for the disaster victims to reduce the associated risks. For the distribution tasks, an effective and efficient relief items distribution model is essential to generate relief items distribution schedules to...

Persistent link: https://www.econbiz.de/10014336753

A genetic algorithm model for optimizing vehicle routing problems with perishable products under time-window and quality requirements

Agrawal, Anil Kumar; Yadav, Susheel; Gupta, Amit Ambar; … - In: Decision analytics journal 5 (2022), pp. 1-14

The Vehicle Routing Problem (VRP) has recently piqued the interest of researchers seeking to improve the efficiency and efficacy of the transportation system in distributing commodities. Many scholars have proposed using a heterogeneous fleet in vehicle routing to minimize distribution costs...

Persistent link: https://www.econbiz.de/10014336906

A step-size follow-the-leader optimization algorithm with an improved step parameters

Singh, Priyanka; Kottath, Rahul - In: Decision analytics journal 9 (2023), pp. 1-16

flock. FTL has been successfully implemented to solve power prediction problems. However, the probability of falling in …

Persistent link: https://www.econbiz.de/10014533154

A crossover-based multi-objective discrete particle swarm optimization model for solving multi-modal routing problems

Afrasyabi, Parastoo; Mesgari, Mohammad Saadi; … - In: Decision analytics journal 9 (2023), pp. 1-17

Passengers must use a set of modes and vehicles to reach their destination in complicated urban structures. Choosing an optimal route is a complicated optimization problem for these passengers. This study proposes a multi-objective optimization algorithm to solve the routing problem in the urban...

Persistent link: https://www.econbiz.de/10014533324

A modified Particle Swarm Optimization algorithm with enhanced search quality and population using Hummingbird Flight patterns

Zare, Mohsen; Akbari, Mohammad-Amin; … - In: Decision analytics journal 7 (2023), pp. 1-24

This study proposes a modified Particle Swarm Optimization (PSO) algorithm based on Hummingbird Flight (HBF) patterns to enhance the search quality and population diversity. The HBF has five concepts: (1) Smaller steps toward position updating are more likely than larger ones, (2) Position...

Persistent link: https://www.econbiz.de/10014505228