Article
Management
Nathan Phelps, Adam Metzler
Summary: The efficient frontier allows investors to maximize returns for a given risk level. Cardinality constrained efficient frontiers (CCEFs) impose an upper bound on the number of assets in the portfolio. A new algorithm was developed to find CCEFs, which performs well but struggles with certain situations involving bonds and equities. We modified the algorithm to improve CCEFs, although this comes with longer runtimes.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Computer Science, Artificial Intelligence
Xuanyu Zheng, Changsheng Zhang, Bin Zhang
Summary: This paper presents a novel metaheuristic algorithm based on the Mayfly algorithm to solve the cardinality constrained mean-variance portfolio optimization problem. The experimental results show that the proposed approach achieves competitive performance on datasets of different sizes, demonstrating the feasibility of this approach in solving the problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Man-Fai Leung, Jun Wang
Summary: This paper presents a collaborative neurodynamic optimization approach for cardinality-constrained portfolio selection, solving the mixed-integer optimization problem using multiple recurrent neural networks and particle swarm optimization. Experimental results demonstrate the superior performance of this approach in handling stock data compared to other methods.
Article
Computer Science, Artificial Intelligence
Shan Lu, Ning Zhang, Lifen Jia
Summary: This paper addresses a multiobjective multiperiod portfolio selection problem based on uncertainty theory, proposing a new uncertain portfolio optimization model and a hybrid technique called the MFA-SOS algorithm to solve it. Various constraints are taken into account in the model, and a numerical example demonstrates the effectiveness of the proposed approach.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Mualla Gonca Avci, Mustafa Avci
Summary: This study extends the EVaR optimization model with practical constraints and compares its risk-adjusted return performances with CVaR model using historical data, demonstrating the potential benefits of using EVaR model in practical investment decisions.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Management
Francisco Guijarro, Prodromos E. Tsinaslanidis
Summary: This article proposes a methodology to address the mean-variance optimisation frontier problem with realistic constraints by hybridising a heuristic algorithm with an exact solution approach. The algorithmic framework generates a constrained frontier that actually fulfils the bound and cardinality constraints, unlike other proposals.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
Derya Deliktas, Ozden Ustun
Summary: This study proposes an integrated approach incorporating fuzzy multi-objective decision-making method and classical mean-variance models for stock evaluation and portfolio optimization. A multi-objective genetic algorithm is employed with three different scalarization techniques for solving the problem. Computational results indicate that the multi-objective genetic algorithm with weighted-sum performs better in most cases.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Tunchan Cura
Summary: This study presents a heuristic approach to portfolio optimization problem using artificial bee colony technique. The results show that the proposed artificial bee colony approach is relatively efficient and effective in solving the problem.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Ilgim Yaman, Turkan Erbay Dalkilic
Summary: Portfolio Optimization is a method to select the best portfolio for an investor, aiming to minimize risk and maximize return. This study proposes a hybrid approach based on Nonlinear Neural Network and Genetic Algorithm to solve the portfolio optimization problem.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Management
David I. Graham, Matthew J. Craven
Summary: Real-world portfolio optimisation problems, often NP-hard, can be efficiently solved using a deterministic method to decompose efficient frontiers into subfrontiers calculated by a quadratic programming algorithm, offering a practical alternative to randomised algorithms. This method can also be applied to other classes of portfolio problems with varying risk measures, and the identified subfrontiers closely correspond to local optima of an evolutionary algorithm's objective function in a case study.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Mathematics, Applied
Hyeong-Ohk Bae, Seung-Yeal Ha, Myeongju Kang, Hyuncheul Lim, Chanho Min, Jane Yoo
Summary: In this paper, a predictor-corrector type Consensus Based Optimization (CBO) algorithm was proposed for solving the portfolio optimization problem in finance, demonstrating success in finding the optimal value.
APPLIED MATHEMATICS AND COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Man-Fai Leung, Jun Wang, Hangjun Che
Summary: This paper investigates portfolio selection based on neurodynamic optimization. It formulates the portfolio selection problem as a biconvex optimization problem and addresses the cardinality-constrained portfolio selection problem as well. A two-timescale duplex neurodynamic approach is customized and applied to solve the reformulated portfolio optimization problem. Experimental results demonstrate the superior performance of the neurodynamic optimization approach compared to three baseline methods in terms of risk-adjusted performance and portfolio returns.
Article
Computer Science, Artificial Intelligence
Wei Chen, Haoyu Zhang, Mukesh Kumar Mehlawat, Lifen Jia
Summary: The success of portfolio construction relies on future stock market performance, with recent advances in machine learning offering significant opportunities. This study introduces a novel approach that combines machine learning for stock prediction and the MV model for portfolio selection, showing superior results in returns and risks compared to traditional methods.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Hong Zhao, Zong-Gan Chen, Zhi-Hui Zhan, Sam Kwong, Jun Zhang
Summary: This paper investigates the multi-objective cardinality constrained portfolio optimization problem and proposes a multiple populations co-evolutionary particle swarm optimization algorithm to address this issue. The algorithm has advantages in dealing with cardinality constraints and multi-objective challenges through strategies such as hybrid encoding, heuristic method, local search, and elite competition.
Article
Mathematics
Stephanie S. W. Su, Sie Long Kek
Summary: The improved Adam algorithm (AdamSE algorithm) presented in this paper shows a smaller number of iterations and faster convergence rate when solving the portfolio optimization problem.
JOURNAL OF MATHEMATICS
(2021)
Review
Computer Science, Artificial Intelligence
Chnoor M. Rahman, Tarik A. Rashid, Abeer Alsadoon, Nebojsa Bacanin, Polla Fattah, Seyedali Mirjalili
Summary: This paper provides a comprehensive investigation of the dragonfly algorithm in the engineering field. It discusses the overview and modifications of the algorithm, surveys its applications in engineering, and compares its performance with other algorithms. The results show that the dragonfly algorithm performs excellently in small to intermediate applications. The purpose of this research is to assist other researchers in studying and utilizing the algorithm to optimize engineering problems.
EVOLUTIONARY INTELLIGENCE
(2023)
Review
Energy & Fuels
Nebojsa Bacanin, Catalin Stoean, Miodrag Zivkovic, Miomir Rakic, Roma Strulak-Wojcikiewicz, Ruxandra Stoean
Summary: An effective energy oversight is a global concern, especially with recent increasing stringency. Machine learning and deep learning approaches have shown high accuracy in energy load and consumption prediction, but few recent methods focus on parameter tuning for better results. This study develops and tunes a long short-term memory (LSTM) DL model for multivariate time-series forecasting of electricity load, using a benchmark dataset from Europe. The results serve as a benchmark for hybrid LSTM-optimization methods in energy time-series forecasting. The study highlights the importance of parameter tuning for improved results using metaheuristics, with the worst-performing metaheuristic still outperforming grid search.
Article
Computer Science, Information Systems
K. Venkatachalam, Zaoli Yang, Pavel Trojovsky, Nebojsa Bacanin, Muhammet Deveci, Weiping Ding
Summary: Human activity recognition (HAR) is an emerging field that identifies human actions in different settings. This study proposes a hybrid model combining one-dimensional convolutional neural network and long short term memory (LSTM) classifier to improve the performance of HAR. The UCI-HAR dataset is used for experimental research.
INFORMATION SCIENCES
(2023)
Article
Engineering, Electrical & Electronic
Marko Sarac, Milos Mravik, Dijana Jovanovic, Ivana Strumberger, Miodrag Zivkovic, Nebojsa Bacanin
Summary: COVID-19 is a respiratory system disorder that has caused pneumonia outbreaks globally. Computed tomography (CT) has played a crucial role in diagnosing the disease. This study proposes a deep learning model called DEPNet to predict COVID-19 cases based on CT images.
JOURNAL OF ELECTRONIC IMAGING
(2023)
Article
Computer Science, Information Systems
Milos Mravik, Marko Sarac, Nebojsa Bacanin, Sasa Adamovic
Summary: This paper examines the impact of new approaches to distance learning during the Covid-19 pandemic, with a focus on comparing the quality of online teaching to face-to-face teaching. The presented results are based on empirical research conducted over a period of 2 years with a large group of students. The study finds that both professors and students encountered self-imposed obstacles, as well as pedagogical, technical, and financial or organizational barriers. The obtained results are further supported by conducting relevant hypothesis tests.
JOURNAL OF INTERNET TECHNOLOGY
(2023)
Article
Multidisciplinary Sciences
M. G. Dinesh, Nebojsa Bacanin, S. S. Askar, Mohamed Abouhawwash
Summary: Pancreatic cancer is often diagnosed at an advanced stage, leading to high mortality rates. Therefore, automated systems that can detect cancer early are crucial. This research aims to predict pancreatic cancer early using deep learning and metaheuristic techniques, analyzing medical imaging data and identifying vital features and cancerous growths.
SCIENTIFIC REPORTS
(2023)
Article
Multidisciplinary Sciences
Nebojsa Bacanin, Nebojsa Budimirovic, K. Venkatachalam, Hothefa Shaker Jassim, Miodrag Zivkovic, S. S. Askar, Mohamed Abouhawwash
Summary: With the rapid growth of stored data in datasets, extracting crucial information becomes difficult. This research presents a novel quasi-reflection learning algorithm - firefly search, an enhanced version of the original arithmetic optimization algorithm. The proposed algorithm is tested on benchmark functions, standard datasets, and a Corona disease dataset, and the experimental results verify its improvements and statistical significance.
Article
Archaeology
Ruxandra Stoean, Nebojsa Bacanin, Catalin Stoean, Leonard Ionescu, Miguel Atencia, Gonzalo Joya
Summary: The accurate assessment of material composition and degradation in newly discovered archaeological artefacts is crucial for decision-making in the restoration and conservation stages. This study proposes a computational framework based on deep learning techniques that can automatically determine the chemical concentration of the predominant metal from microscope images and identify corrosion spots specific to that metal. The results suggest that the artificial intelligence framework can provide on-site support for early examination of metal heritage assets, even with limited training data.
JOURNAL OF CULTURAL HERITAGE
(2023)
Article
Computer Science, Software Engineering
Luka Jovanovic, Dijana Jovanovic, Milos Antonijevic, Bosko Nikolic, Nebojsa Bacanin, Miodrag Zivkovic, Ivana Strumberger
Summary: This research proposes a hybrid approach based on an improved metaheuristics algorithm to optimize the XGBoost machine learning model for enhancing Web security. Evaluations on three publicly available phishing website datasets show that the proposed solution outperforms other methods and represents a perspective solution in the domain of web security.
JOURNAL OF WEB ENGINEERING
(2023)
Article
Environmental Sciences
Charli Sitinjak, Vladimir Simic, Rozmi Ismail, Nebojsa Bacanin, Charles Musselwhite
Summary: Effective end-of-life vehicle (ELV) management is crucial for minimizing the environmental and health impacts of Indonesia's growing automotive industry. However, proper ELV management has received limited attention. Our qualitative study identified barriers to effective ELV management in Indonesia's automotive sector, including inadequate regulation and enforcement, insufficient infrastructure and technology, low education and awareness, and a lack of financial incentives. We recommend a comprehensive and integrated approach involving coordination among government, industry, and stakeholders to address these barriers and develop sustainable ELV management policies and decisions.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Computer Science, Information Systems
B. Saravana Balaji, Wieslaw Paja, Milos Antonijevic, Catalin Stoean, Nebojsa Bacanin, Miodrag Zivkovic
Summary: Smart cities consist of intelligent industrial devices that improve people's lives and save lives. Intelligent remote patient monitoring predicts the patient's condition. Internet of Things (IoT), artificial intelligence (AI), and cloud computing have enhanced the healthcare industry. Edge computing accelerates patient data transmission and ensures latency, reliability, and response time. However, the transmission of large amounts of patient data may lead to IoT data security vulnerabilities, posing concerns and challenges. This research proposes a secure, scalable, and responsive patient monitoring system. The model uses lightweight attribute-based encryption (LABE) to encrypt and decrypt IoT patient data for cloud-based protection. Edge servers are situated between the IoT and cloud for improved quality of service (QoS) and patient diagnosis. The deep belief network (DBN) predicts and monitors patient health, while the bat optimization algorithm (BOA) optimizes hyperparameters. Deep belief is used to identify hyperparameters, and BOA is applied for optimization. Swarm intelligence enhances prediction results and edge-cloud reaction time. A simulated environment evaluates the secure patient health monitoring system for efficiency, security, and efficacy. The proposed model offers effective patient remote health monitoring through a secure edge-cloud-IoT environment, with improved accuracy (97.9%), precision (95.6%), recall (94.6%), F1-score (94.9%), and false discovery rate (0.06%).
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Sara Boskovic, Libor Svadlenka, Stefan Jovcic, Momcilo Dobrodolac, Vladimir Simic, Nebojsa Bacanin
Summary: This paper introduces a new subjective technique called FullEX for evaluating the importance of criteria in LMD courier selection. Through evaluation, on-time delivery is considered the most important criterion for sustainable LMD courier selection.
Article
Chemistry, Multidisciplinary
Mohamed Salb, Luka Jovanovic, Nebojsa Bacanin, Milos Antonijevic, Miodrag Zivkovic, Nebojsa Budimirovic, Laith Abualigah
Summary: This paper addresses the critical security challenges in the internet of things (IoT) landscape by proposing an innovative solution that combines convolutional neural networks (CNNs) and the XGBoost model for intrusion detection. By customizing the reptile search algorithm for hyperparameter optimization, the methodology provides a resilient defense against emerging threats in IoT security. The introduced algorithm constructed models with the best performance in both experiments, and its outcomes have been statistically evaluated and analyzed for feature importance using Shapley additive explanations.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Mihailo Todorovic, Nemanja Stanisic, Miodrag Zivkovic, Nebojsa Bacanin, Vladimir Simic, Erfan Babaee Tirkolaee
Summary: This study aims to create a machine learning model that can predict opinions in external audits and surpass the benchmark set in prior research. The study compares the performance of different algorithms in two scenarios and finds improvement through optimized hyperparameter tuning and the use of an iterative algorithm.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Sara Boskovic, Libor Svadlenka, Stefan Jovcic, Momcilo Dobrodolac, Vladimir Simic, Nebojsa Bacanin
Summary: This article introduces a new multi-criteria decision-making method, called the AROMAN method, which combines linear and vector normalization techniques to obtain accurate data structures and develops an original final ranking equation. Comparative analysis shows a high level of confidence and stability in the AROMAN method in the decision-making field.
