Harris Hawks Optimization Algorithm: Variants and Applications

Shehab, Mohammad; Mashal, Ibrahim; Momani, Zaid; Shambour, Mohd Khaled Yousef; Al-Badareen, Anas Bassam; Al-Dabet, Saja; Bataina, Norma; Alsoud, Anas Ratib; Abualigah, Laith

doi:10.1007/s11831-022-09780-1

Cited by 53 publications

(26 citation statements)

References 144 publications

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“…Relays 3. Sidechain Implementation of particular cross-blockchain methods such as these mechanisms is the technological option for integration [27].…”

Section: Mechanisms Of Cross Blockchainmentioning

confidence: 99%

“…Subsequently, the trusted entities intricating in the endorsement procedure, and entities expect a similar method of integration is appropriate like a centralized method. Optimization algorithms [32] included in the process are detailed during discussion Stage 4: Integration mode selection At this phase, integration mode performs like superseding the situation thus enabling direct communiqué amid blockchain networks. The associated networks have diverse technologies underlying the architecture, nevertheless the applications remain premeditated for a precise resolution thru a definite value type.…”

Section: Stage 1: the Application Domain Selectionmentioning

confidence: 99%

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Cross-Chain Interoperability - Validating Smart Contracts to Interoperate Over Diverse Blockchain Networks Using Interoperable Blockchain Framework Design (IBFD)

Helal

Alsoud

Alshareef

2022

Preprint

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Blockchain technology is meant to perform a potential act to intensify the performance of several information systems. In this view, the platforms and the applications adapted for blockchain must be competitive to communicate and connect. The current blockchain platforms have many limitations, such as interoperability issues in divergent systems. The present platforms of blockchain applications work only within their networks. Blockchain interoperability allows data and values to be sent across various networks. The fundamental technology may be homogeneous, still, it relies on third-party intermediaries using the centralized mechanism to retrieve or exchange data from various interacting blockchain networks. The existing intermediaries incorporate security and trust by maintaining a centralized ledger to monitor ‘account balances’ and verify a transaction’s authenticity. The incompetence of individual and independent blockchains to interact with each other is an ingrained issue in decentralized systems. Cross-Chain Interoperability Protocol (CCIP) and similar mechanisms support networks like Fabric, Ripple and Ethereum to interrelate and interoperate each other. It is identified that the shortfall in such significant inter-blockchain communication creates a hassle for the mainstream adoption of blockchain. The article discusses different techniques and methods that provide cross-chain interoperability, its significant features and its applications in blockchain technology. It proposes the Inter-operable Blockchain Framework Design (IBFD), a cross blockchain architecture for interoperability of blockchain networks.

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“…Relays 3. Sidechain Implementation of particular cross-blockchain methods such as these mechanisms is the technological option for integration [27].…”

Section: Mechanisms Of Cross Blockchainmentioning

confidence: 99%

Section: Stage 1: the Application Domain Selectionmentioning

confidence: 99%

Cross-Chain Interoperability - Validating Smart Contracts to Interoperate Over Diverse Blockchain Networks Using Interoperable Blockchain Framework Design (IBFD)

Helal

Alsoud

Alshareef

2022

Preprint

Self Cite

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“…DL enabled the building of predictive models for the early diagnosis of tumor disease, and scientists used proven pattern analysis methods. DL algorithms outperformed traditional machine learning due to their highly accurate results [13][14][15]. Also, it often matches or surpasses human performance.…”

Section: Introductionmentioning

confidence: 95%

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

Alzu’bi

Abdullah

Hmeidi

et al. 2022

Journal of Healthcare Engineering

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Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist’s workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models; three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results; they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist’s workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease’s track and preserve the patient’s life.

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“…In the optimization algorithms, behaviours of different organisms are considered as the source of proposing new algorithms [1]. For instance, the Particle swarm optimization (PSO) algorithm mimetic the school of fish and birds [2], Reptile Search Algorithm (RSA) [3] is inspired by simulating the conduct of Crocodiles in life, and Harris Hawks Optimization (HHO) [4] inspired from Harris hawks' in hunting escaping preys.…”

Section: Introductionmentioning

confidence: 99%

Improved Harris Hawks Optimization Algorithm for Solving Real Engineering Optimization Problems

Daoud

Shehab

Abualigah

et al. 2023

Preprint

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Harris Hawks Optimization (HHO) algorithm was proposed recently under the metaheuristic algorithms, which can fix many problems in various domains. However, it needs to improve in local search, which may lead to a loss of diversity, stuck in a local minimum, which procures premature convergence. Two steps have been introduced in this paper to avoid these issues. Firstly, integrating Opposition-based learning (OBL) with HHO accelerates the search process and enhances the choice of better solutions, namely OHHO. Then, using reinforcement learning to enhance the research technique of OHHO, called RLOHHO. CEC 2015 and CEC 2017 benchmark functions and real engineering problems are utilized to evaluate the efficiency. Finally, the proposed versions of HHO are compared with efficient optimization algorithms. The experiment results illustrate that the RLOHHO's version achieved better solutions than the original HHO, OHHO, and other similar published algorithms in the literature.

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Harris Hawks Optimization Algorithm: Variants and Applications

Cited by 53 publications

References 144 publications

Cross-Chain Interoperability - Validating Smart Contracts to Interoperate Over Diverse Blockchain Networks Using Interoperable Blockchain Framework Design (IBFD)

Cross-Chain Interoperability - Validating Smart Contracts to Interoperate Over Diverse Blockchain Networks Using Interoperable Blockchain Framework Design (IBFD)

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

Improved Harris Hawks Optimization Algorithm for Solving Real Engineering Optimization Problems

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