This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing of neural networks (NNs). DEL determines the size ensemble, number individual NNs employing constructive strategy, hidden nodes constructive–pruning and different training samples NN's learning. For diversity, negative correlation has been introduced also variation made that provide better from whole samples. The major benefits proposed compared to existing algorithms are (1) automatic design ensemble; (2)...

Human facial emotion recognition (FER) has attracted the attention of research community for its promising applications. Mapping different expressions to respective emotional states are main task in FER. The classical FER consists two major steps: feature extraction and recognition. Currently, Deep Neural Networks, especially Convolutional Network (CNN), is widely used by virtue inherent mechanism from images. Several works have been reported on CNN with only a few layers resolve problems....

This paper presents an overview of significant advances made in the emerging field nature-inspired computing (NIC) with a focus on physics- and biology-based approaches algorithms. A parallel development past two decades has been emergence computational intelligence (CI) consisting primarily three fields neural networks, evolutionary fuzzy logic. It is observed that NIC CI intersect. The authors advocate foresee more cross-fertilisation fields.

This paper presents a review of research reported on simulated annealing (SA). Different cooling/annealing schedules are summarized. Variants SA delineated. Recent applications in engineering reviewed.

Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements executed movements. Decoding observed or imagined been demonstrated with limited success only reported in few studies. normally use low delta (~1 Hz) band for reconstructing an imagined/observed movement. In contrast to...

In light of the pandemic, identification and processing COVID-19-related text have emerged as critical research areas within field Natural Language Processing (NLP). With a growing reliance on online portals social media for information exchange interaction, surge in textual content, comprising disinformation, misinformation, fake news, rumors has led to phenomenon an infodemic World Wide Web. Arabic, spoken by over 420 million people worldwide, stands significant low-resource language,...

In the past three decades nature-inspired and meta-heuristic algorithms have dominated literature in broad areas of search optimization. Harmony algorithm (HSA) is a music-inspired population-based optimization algorithm. The concept behind to find perfect state harmony determined by aesthetic estimation. This paper starts with an overview harmonic phenomenon music improvisation used musicians how it applied problem. memory its mathematical implementation are introduced. A review HSA...

In recent years, the amount of digital text contents or documents in Bengali language has increased enormously on online platforms due to effortless access Internet via electronic gadgets. As a result, an enormous unstructured data is created that demands much time and effort organize, search manipulate. To manage such massive number effectively, intelligent document classification system proposed this paper. Intelligent resource-constrained (like Bengali) challenging unavailability...

Covid text identification (CTI) is a crucial research concern in natural language processing (NLP). Social and electronic media are simultaneously adding large volume of Covid-affiliated on the World Wide Web due to effortless access Internet, gadgets outbreak. Most these texts uninformative contain misinformation, disinformation malinformation that create an infodemic. Thus, essential for controlling societal distrust panic. Though very little Covid-related (such as disinformation,...

Gravitational search algorithm (GSA) is a nature-inspired conceptual framework with roots in gravitational kinematics, branch of physics that models the motion masses moving under influence gravity. In GSA, collection objects interacts each other Newtonian gravity and laws motion. The performances are measured by masses. All these attract force, while this force causes global movement all toward heavier position object corresponds to solution problem. positions updated every iteration best...

This article presents a review of novel nature-inspired adaptive optimization algorithm known as Water Drop Algorithm (WDA) which imitates the dynamics river systems and behavior water drops when they are moving in system such variation velocity, change sediment/soil bed, direction flow, so on. Recent improved, modified, WDAs examined this paper. Various applications WDA described briefly including vehicle routing problem, economic load dispatch emission optimal data aggregation tree...

The spiral has universal appeal and a mysterious resonance with the human spirit. It is intriguing beautiful. pattern found extensively in nature, encoded into plants, animals, weather phenomena, earth, galaxies around us. paper starts mathematical representation of phenomena. Next, 2-dimensional n-dimensional Spiral Dynamics Optimization (SpDO) algorithms are presented followed by their variants such as adaptive SpDO hybrid SpDO. Finally, applications to engineering problems reviewed...

Eye cataract is a condition in which the lens of eye becomes clouding or less transparent. This affects clear vision and most prevailing causes blindness. Therefore, early detection prevention may reduce blindness rate surgery pain patients. paper presents an system using Deep Convolution Neural Network (DCNNs) comprising two modules: training testing. The proposed DCNNs architecture trained, validated tested with retinal fundus images. Experimental result shows that capable detecting high accuracy.

Road boundary estimation is an essential task for autonomous vehicles and intelligent driving assistants. It considerably straightforward to attain the when roads are marked properly with indicators. However, estimating road reliably without prior knowledge of road, such as markings, extremely difficult. This paper proposes a method estimate boundaries in different environments deep learning-based semantic segmentation, any predefined markings. The proposed employed encoder-decoder-based...