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RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

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JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY 2024年第10期150卷 455页

作者： Yaqoob, Abrar Verma, Navneet Kumar Aziz, Rabia Musheer Shah, Mohd Asif VIT Bhopal Univ Sch Adv Sci & Language Bhopal 466114 Madhya Pradesh India State Planning Inst Planning Dept New Div Lucknow 226001 Utter Pradesh India Kardan Univ Dept Econ Parwane Du Kabul 1001 Afghanistan Lovely Profess Univ Div Res & Dev Phagwara 144001 Punjab India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification *** study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data's high dimensionality and *** introduce a novel hybrid gene selection approach that combines the Harris Hawk optimization (HHO) and whale optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model's performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, *** proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy *** findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

关键词： Breast cancer Harris Hawk algorithm whale optimization algorithm Deep learning

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Nonlinear Vibration Analysis of Laminated Composite Cylindrical Shell Under External Loading Utilizing Meta-Heuristic optimization algorithms

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF MECHANICAL ENGINEERING 2024年第2期48卷 757-777页

作者： Duc, Nguyen Dinh Foroutan, Kamran Varedi-Koulaei, Seyyed Mojtaba Ahmadi, Habib VNU Hanoi Univ Engn & Technol Fac Civil Engn 144 Xuan Thuy St Hanoi Vietnam Shahrood Univ Technol Fac Mech Engn POB 316 Shahrood Iran

This paper investigates nonlinear vibration analysis of laminated composite cylindrical shells under external loading considering three, four, and five layers. The objective of this study is to optimize fiber angles through the utilization of various meta-heuristic optimization algorithms. The basic equations are derived by the classical shell theory combined with von-Karman's nonlinearity. These equations are subsequently solved using the Galerkin method. In the optimization problem, two objective functions are considered including the minimization of vibration amplitude and the maximization of natural frequency. Moreover, in the final optimization process, both objective functions are simultaneously considered. To tackle these optimization problems, two swarm-based metaheuristic algorithms, namely particle swarm optimization (PSO) and whale optimization algorithm (WOA), are employed. The WOA algorithm yields more suitable results for cases with three and four layers, while the PSO algorithm performs better in the five layers case. Thus, considering the antiquity and power of the PSO algorithm, the most famous and powerful swarm-based optimization algorithm, the WOA algorithm's capabilities can also be confirmed for this optimization problem.

关键词： Nonlinear vibration analysis Laminated composite cylindrical shell Particle swarm optimization whale optimization algorithm Optimal fiber angles

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A novel contact optimization algorithm for endomicroscopic surface scanning

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2024年第10期19卷 2031-2041页

作者： Xu, Xingfeng Zhao, Shengzhe Gong, Lun Zuo, Siyang Tianjin Univ Key Lab Mech Theory & Equipment Design Minist Educ Tianjin 300072 Peoples R China

PurposeProbe-based confocal laser endomicroscopy (pCLE) offers real-time, cell-level imaging and holds promise for early cancer diagnosis. However, a large area surface scanning for image acquisition is needed to overcome the limitation of field-of-view. Obtaining high-quality images during scanning requires maintaining a stable contact distance between the tissue and probe. This work presents a novel contact optimization algorithm to acquire high-quality pCLE *** contact optimization algorithm, based on swarm intelligence of whale optimization algorithm, is designed to optimize the probe position, according to the quality of the image acquired by probe. An accurate image quality assessment of total co-occurrence entropy is introduced to evaluate the pCLE image quality. The algorithm aims to maintain a consistent probe-tissue contact, resulting in high-quality images *** experiments on sponge, ex vivo swine skin tissue and stomach tissue demonstrate the effectiveness of the contact optimization algorithm. Scanning results of the sponge with three different trajectories (spiral trajectory, circle trajectory, and raster trajectory) reveal high-quality mosaics with clear details in every part of the image and no blurred *** contact optimization algorithm successfully identifies the optimal distance between probe and tissue, improving the quality of pCLE images. Experimental results confirm the high potential of this method in endomicroscopic surface scanning.

关键词： Probe-based confocal laser endomicroscopy Endomicroscopic surface scanning Contact optimization control whale optimization algorithm

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Optimal Design of IIR-Type Fractional Order Digital Integrator Using Mayfly optimization algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第2期42卷 913-942页

作者： Dey, Souvik Roy, Provas Kumar Chakraborty, Sudipta Bengal Inst Technol Dept Elect & Commun Engn Kolkata 700150 W Bengal India Kalyani Govt Engn Coll Dept Elect Engn Kalyani 741235 W Bengal India Techno Main Saltlake Dept Elect & Commun Engn Kolkata 700091 W Bengal India

This paper represents the execution of a reliable, steady and wideband fractional order digital integrator (FODI) in respect of infinite impulse response (IIR) filter using a recent nature inspired meta-heuristic optimization technique known as mayfly optimization algorithm (MOA). MOA significantly functions stronger against some of the popular algorithms like real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), improved particle swarm optimization (IPSO), whale optimization algorithm (WOA) and chaotic oppositional whale optimization algorithm (COWOA) with reference to different magnitude error performances, explanation character accuracy, convergence profile and computational time required to find the optimal solution. In this paper, the authors thoroughly analyze the convergence behavior and magnitude error metrices of the different order FODI's using WOA, COWOA and MOA. MATLAB simulation results also powerfully uphold the magnitude response efficiency and stability of the proposed different order FODI's over some recently published research papers.

关键词： Digital signal processing Infinite impulse response Fractional order integrator whale optimization algorithm Chaotic oppositional whale optimization algorithm Mayfly optimization algorithm

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Evaluating the Performance of various algorithms for Wind Energy optimization: A Hybrid Decision-Making model

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 221卷

作者： Ala, Ali Mahmoudi, Amin Mirjalili, Seyedali Simic, Vladimir Pamucar, Dragan Shanghai Jiao Tong Univ Sch Mech Engn Dept Ind Engn & Management Shanghai 200240 Peoples R China Southeast Univ Sch Civil Engn Dept Construction & Real State Nanjing 210096 Peoples R China Torrens Univ Ctr Artificial Intelligence Res & Optimizat Brisbane Australia Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11010 Serbia Univ Belgrade Fac Org Sci Dept Operat Res & Stat Jove Ilica 154 Belgrade 11000 Serbia Yuan Ze Univ Coll Engn Taoyuan City Taiwan Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary Yonsei Univ Yonsei Frontier Lab Seoul South Korea

Wind resource is one of the most promising renewable energy, which has become a suitable replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is essential to obtain the maximum power output as other variables are uncontrollable. This paper presents four different optimization algorithms, namely ant lion optimization (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and crow search optimization (CSO), considering a hybrid decision-making model to compare the performances of wind energy optimization. In the first phase, the evolutionary algorithms are defined based on several factors to meet the need for wind energy based on volumetric and time reliability, reversibility, and vulnerability as well as evaluate optimized energy to the subscriber from the Gansu region. In the second phase, the ordinal priority approach (OPA) is coupled with VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the evolutionary algorithms. Then, the results are compared with the absolute optimal response based on the nonlinear programming method obtained from GAMS software. The results demonstrate that an ALO out-performs other algorithms. The average accuracy of ALO is 92%. CSO is the least accurate with 55% of the absolute optimal response. ALO is found to be faster, more efficient, and achieved economy and reliability as compared to other optimization algorithms for solving the problem under consideration. It is shown that the applied models are robust, effective, and able to save costs.

关键词： Renewable energy systems Absolute optimal Decision -making analysis Planning control Ordinal priority approach optimization algorithm whale optimization algorithm

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optimization of porous carbons for methane adsorption from South African coal wastes

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INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION 2023年第2期43卷 264-287页

作者： Abdulsalam, Jibril Lawal, Abiodun Ismail Ozonoh, Maxwell Onifade, Moshood Bada, Samson Mulopo, Jean Univ Witwatersrand Fac Engn & Built Environm Sch Chem & Met Engn Johannesburg South Africa Inha Univ Yong Hyun Dong Dept Energy Resources Engn Incheon South Korea Fed Univ Technol Akure Dept Min Engn Akure Nigeria Univ Namibia Dept Civil & Min Engn Windhoek Namibia

The preparation of porous carbons by KOH activation from three different coal wastes in South Africa was modeled and optimized using an artificial neural network and whale optimization algorithm (ANN-WOA). The potential for methane adsorption of the optimized porous carbons was investigated. The optimal conditions for preparing the porous carbons, as determined by maximum surface area and micropore volume, according to ANN-WOA results, were as follows: Temperature of reaction: 800 degrees C;activation time: 120 minutes;impregnation ratio: 1:4. SEM/EDS and BET methods were used to examine the surface properties and structural morphology of the optimized activated carbons. The methane adsorption isotherm on the three optimized porous carbons was fitted with the Toth isotherm model, which had an average R-2 value of 0.9999. The maximum adsorption capacity of the three optimized porous carbons were 158.4, 147.6, and 123.1 cm(3)/g at 25 degrees C and an average pressure of 37 bar. In addition, because of their high surface area and methane adsorption capacity, these materials have the potential to be used in natural gas storage, demonstrating that coal wastes in South Africa have the potential to be used as a sustainable starting material for the synthesis of porous carbons for gas storage.

关键词： Activated carbon artificial neural network methane adsorption micropore volume surface area whale optimization algorithm

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Scheduling optimization of wind-thermal interconnected low-carbon power system integrated with hydrogen storage

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第50期30卷 109354-109371页

作者： Wang, Haifeng Ma, Xiaoran Zhao, Xingyu Wang, Weijun North China Elect Power Univ Dept Econ & Management 689 Huadian Rd Baoding 071003 Peoples R China Jiangsu Keneng Power Engn Consulting Co Ltd Dept Syst Consulting Nanjing Peoples R China

To improve the consumption of wind energy and reduce carbon emission, this paper proposes a wind-thermal interconnected low-carbon power system integrated with hydrogen storage. An energy scheduling optimization model aiming at minimizing the daily operation cost of the system is constructed considering environmental operation cost quantification, and whale optimization algorithm is used to optimize multiple variables. Finally, in simulation example, various scenarios are set considering the application way of hydrogen and the scenarios with and without the carbon capture and storage (CCS) are optimized, respectively. The horizontal comparison results show that the system with hydrogen production (S2) and the system with hydrogen fuel cell (S3) have higher economic operation cost than that of wind-thermal interconnected power system only (S1), but the environmental cost is reduced;the system's daily operating costs are reduced. The wind curtailment rate decreases from 11.0% (S1) to 3.8% (S2 and S3) without CCS, and from 9.0% (S1) to 2.1% (S2 and S3) with CCS. The longitudinal comparison shows that the thermal power output is reduced and the wind power consumption is improved with CCS. The addition of CCS increases total operating costs but significantly reduces environmental costs. Configuring hydrogen storage system in the wind-thermal interconnected power system can effectively promote the consumption of wind energy and reduce the system operation cost;however, the utilization of CCS is economic unfriendly at present.

关键词： Hydrogen storage Wind-thermal interconnected system Energy scheduling optimization model whale optimization algorithm CCS Carbon neutrality

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HWMWOA: A Hybrid WMA-WOA algorithm with Adaptive Cauchy Mutation for Global optimization and Data Classification

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2023年第4期22卷 1195-1252页

作者： Zhang, Jiali Li, Haichan Parizi, Morteza Karimzadeh Guangzhou Coll Technol & Business Coll Engn Guangzhou 510800 Guangdong Peoples R China Software Engn Inst Guangzhou Dept Network Technol Guangzhou 510990 Guangdong Peoples R China Tech & Vocat Univ TVU Fac Shahid Chamran Dept Comp Engn Kerman Branch Kerman Iran

Combinatorial metaheuristic optimization algorithms have newly become a remarkable domain for handling real-world and engineering design optimization problems. In this paper, the whale optimization algorithm (WOA) and the Woodpecker Mating algorithm (WMA) are combined as HWMWOA. WOA is an effective algorithm with the advantage of global searching ability, where the control parameters are very less. But WOA is more probable to get trapped in the local optimum points and miss diversity of population, therefore suffering from premature convergence. The fundamental goal of the HWMWOA algorithm is to overcome the drawbacks of WOA. This betterment includes three basic mechanisms. First, a modified position update equation of WMA by efficient exploration ability is embedded into HWMWOA. Second, a new self-regulation Cauchy mutation operator is allocated to the proposed hybrid method. Finally, an arithmetic spiral movement with a novel search guide pattern is used in the suggested HWMWOA algorithm. The efficiency of the suggested algorithm is appraised over 48 test functions, and the optimal outcomes are compared with 15 most popular and newest metaheuristic optimization algorithms. Moreover, the HWMWOA algorithm is applied for simultaneously optimizing the parameters of SVM (Support Vector Machine) and feature weighting to handle the data classification problem on several real-world datasets from the UCI database. The outcomes prove the superiority of the suggested hybrid algorithm compared to both WOA and WMA. In addition, the results represent that the HWMWOA algorithm outperforms other efficient techniques impressively.

关键词： whale optimization algorithm Woodpecker Mating algorithm Cauchy mutation Support Vector Machine data classification

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A Multi-Stage Adaptive Method for Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Swarm Intelligence optimization

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BATTERIES-BASEL 2023年第4期9卷 224-224页

作者： Bao, Qihao Qin, Wenhu Yun, Zhonghua Southeast Univ Sch Instrument Sci & Engn 2 Sipailou Nanjing 210096 Peoples R China

The accuracy of predicting the remaining useful life of lithium batteries directly affects the safe and reliable use of the supplied equipment. Since the degradation of lithium batteries can easily be influenced by different operating conditions and the regeneration and fluctuation of battery capacity during the use of lithium batteries, it is difficult to construct an accurate prediction model of lithium batteries. Therefore, research into high-precision methods of predicting the remaining useful life has been a popular topic for the whole-life management system of lithium batteries. In this paper, a new hybrid optimization method for predicting the remaining useful life of lithium batteries is proposed. The proposed method incorporates two different swarm intelligence optimization algorithms. Firstly, the whale optimization algorithm is used to optimize the variational mode decomposition (WOAVMD), which can decompose the historical life data into several trend components and non-trend components. Then, the sparrow search algorithm is applied to optimize the long short-term memory neural network (SSALSTM) to predict the non-trend component and the autoregressive integrated moving average model (ARIMA) is used to predict trend components. Finally, the prediction results of each component are integrated to evaluate the remaining useful life of lithium batteries. Results show that better prediction accuracy is obtained in the prediction experiments for several types of batteries in both the NASA and CALCE battery datasets. The generalization ability of the algorithm has also been effectively improved owing to the optimization of parameters of the variational mode decomposition (VMD) and the long short-term memory neural network (LSTM).

关键词： battery remaining useful life whale optimization algorithm variational mode decomposition sparrow search algorithm long short-term memory

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optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence algorithms

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Computers, Materials & Continua 2023年第5期75卷 4341-4355页

作者： Siling Feng Yinjie Chen Mengxing Huang Feng Shu School of Information and Communication Engineering Hainan UniversityNo.58 Renmin AvenueHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityNo.58 Renmin AvenueHaikou570228China

Due to their adaptability,Unmanned Aerial Vehicles(UAVs)play an essential role in the Internet of Things(IoT).Using wireless power transfer(WPT)techniques,an UAV can be supplied with energy while in flight,thereby extending the lifetime of this energy-constrained *** paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed *** this paper,we propose an optimization strategy for the resource allocation of UAVs in sensor communication *** is a practical solution to the problem of marine sensor networks that are located far from shore and have limited power.A corresponding system model is summarized based on the scenario and existing theoretical *** minimum throughputmaximizing object is then formulated as an optimization *** swarm intelligence algorithms are utilized effectively in numerous fields,this paper chose to solve the formed optimization problem using the Harris Hawks optimization and whale optimization *** paper introduces a method for translating multi-decisions into a row vector in order to adapt swarm intelligence algorithms to the problem,as joint time and energy optimization have two sets of *** proposed method performs well in terms of stability and ***,performance is evaluated through numerical *** results demonstrate that the proposed method performs admirably in the given scenario.

关键词： Resource allocation unmanned aerial vehicles harris hawks optimization whale optimization algorithm

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