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Energy-efficiency is one of the most important design criteria for mobile systems, such as smartphones and tablets. But current mobile systems always over-provision resources to satisfy users. The root cause is that, we have no knowledge on how much of ...

Molecular docking plays a vital role in modern drug discovery, by supporting predictions of the binding modes and affinities of ligands at the binding site of target proteins. Several docking programs have been developed for both commercial and academic ...

COVID-19 is a highly contagious virus that causes respiratory diseases in humans. Responding quickly to such pathogen is crucial to stop the uncontrolled spread of diseases. Through computational approaches, repurposing existing drugs is an ...

Cyber security has received increasing attention, as people use more Internet applications in their lives and worry about the security of their personal data on the Internet. Intrusion Detection Systems (IDSs) are critical security tools that can ...

• EvoBMF: a multi-objective evolutionary DL model for IDS.
• EvoBMF uses BiLSTM, MHA, and FCL for feature extraction and classification.
• NAS technique automates parameter adjustment of EvoBMF.
• SMOTE technique improves recognition ...

The firefly algorithm (FA) has gained widespread attention and has been widely applied because of its simple structure, few control parameters and easy implementation. As the traditional FA lacks a mutation mechanism, it tends to fall ...

• A complex network-based population guidance mechanism is proposed for enhancing the search performance of the FA.

• State-of-the-art financial time series prediction models are described in a detailed manner.
• Various evaluation indicators used in the ML prediction models are discussed and compared.
• Research gaps in this field are identified and ...

Financial time series (FTS) are nonlinear, dynamic and chaotic. The search for models to facilitate FTS forecasting has been highly pursued for decades. Despite major related challenges, there has been much interest in this topic, and many ...

Financial time series forecasting has been an attractive application of machine learning techniques because an advanced forecasting method can help to accurately predict price changes in markets and make good trading profits. In this study, an ...

• An emerging DNM is innovatively applied to financial time series forecasting.
• A specially designed SFDE algorithm is proposed to optimize the DNM.
• The SFDE adopts a scale-free network to guide the search process.
• The DNM-SFDE ...

Research on dendrites has been conducted for decades, providing valuable information for the development of dendritic computation. Creating an ideal neuron model is crucial for computer science and may also provide robust guidance for ...

Our world is moving fast towards the era of the Internet of Things (IoT), which connects all kinds of devices to digital services and brings significant convenience to our lives. With the rapid increase in the number of devices connected to the ...

The visual system plays a vital role when the brain receives and processes information. Approximately ninety percent of the information received by the brain comes from the visual system, and motion detection is a crucial part of ...

Dendritic neuron model (DNM), which is a single neuron model with a plastic structure, has been applied to resolve various complicated problems. However, its main learning algorithm, namely the back-propagation (BP) algorithm, suffers ...

In 2020, a novel coronavirus disease became a global problem. The disease was called COVID-19, as the first patient was diagnosed in December 2019. The disease spread around the world quickly due to its powerful viral ability. To date, ...

• A dendritic neural regression is applied to predict the COVID-19 transmission trend.

Advances in the understanding of dendrites promote the development of dendritic computation. For decades, the researchers are committed to proposing an appropriate neural model, which may feedback the research on neurons. This paper aims to employ ...

Wind energy is considered one of the fastest growing renewable ('green') energy resources. Precise wind power forecasting is imperative to ensure reliable power system planning and wind farm operation. However, traditional methods cannot ...

Inspired by the dynamic dendritic architecture of biological neurons, the approximate logic neuron model (ALNM) is a novel model recently proposed by us. ALNM owns four layers, namely, the synaptic layer, the dendritic layer, the membrane layer, ...

This paper proposes a novel plastic neural model (PNM) at the single neuron level and a specified learning algorithm to train it. The dendritic structure of PNM presents its diversity to fulfill each particular task. During the training process, ...

In recent years, a number of recombination operators have been proposed for multiobjective evolutionary algorithms (MOEAs). One kind of recombination operators is designed based on the Gaussian process model. However, this approach only uses one ...

In this paper, an evolutionary dendritic neuron model (EDNM) is proposed to solve classification problems. It utilizes synapses and dendritic branches to implement the nonlinear computation. Distinct from the classical dendritic neuron model (CDNM) ...

Most multiobjective particle swarm optimizers (MOPSOs) often face the challenges of keeping diversity and achieving convergence on tackling many-objective optimization problems (MaOPs), as they usually use the nondominated sorting method or ...

Financial bankruptcy prediction is crucial for financial institutions in assessing the financial health of companies and individuals. Such work is necessary for financial institutions to establish effective prediction models to make appropriate lending ...