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Service selection based on blockchain smart contracts in cloud-edge environment

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 13693-13715页

作者： Ning, Yingying Li, Jing Zhu, Ming Liu, Chuanxi Shandong Univ Technol Sch Comp Sci & Technol Zibo 255000 Peoples R China

The rapid integration of cloud computing and edge computing has brought the cloud-edge environment into the spotlight in information technology. Within this context, the selection of high-quality and reliable services is crucial to meet the needs of users. However, ensuring the reliability of service information is a challenge due to its vulnerability to tampering. This research paper proposes a method for service selection in the cloud-edge environment based on blockchain smart contracts. By leveraging blockchain technology, this method achieves decentralized and trustworthy service selection. Through smart contracts, user interactions are securely recorded, significantly reducing the risk of information tampering and enhancing information reliability. Additionally, the arithmetic optimization algorithm is improved for service selection on the blockchain by introducing mutation and crossover operations. Experimental results demonstrate that this method effectively prevents tampering with service information and improves the utility value of selected services compared to traditional methods and metaheuristic algorithms mentioned.

关键词： Cloud computing Edge computing Blockchain Smart contract Service selection arithmetic optimization algorithm

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An improved arithmetic method for determining the optimum placement and size of EV charging stations

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COMPUTERS & ELECTRICAL ENGINEERING 2024年 120卷

作者： Fotis, Georgios Aarhus Univ Ctr Energy Technol Birk Centerpk 15Innovatorium DK-7400 Herning Denmark

The increasing number of electric vehicles (EVs) will result in a rise in electric vehicle charging stations (EVCSs), which will have a significant effect on the electrical grid. One major issue is deciding where to place EVCSs in the power grid in the most optimal way. The distribution network is greatly impacted by inadequate EVCS prediction, which results in issues with frequency and voltage stability. This paper suggests an optimization method called Binary Random Dynamic arithmetic optimization algorithm (BRDAOA) that is applied on an IEEE 33 bus network to determine the best position for EVCSs as efficiently as possible, and the Loss Sensitivity Factor (LSF) was used in the analysis. Considering the system voltage, the load (actual power), and the system losses, LSF was calculated for a variety of buses. The efficacy of the suggested method is demonstrated by a final comparison of its findings with those of the arithmetic optimization algorithm (AOA) and two additional metaheuristic algorithms. In addition to reducing line losses by 2% compared to the AOA method and 4% compared to the other two metaheuristic optimization methods, the suggested optimization approach known as BRDAOA requires less computing time than the other three methods. Finally, a reliability test was conducted to determine the best location for EVCS in the IEEE 33 BUS system.

关键词： arithmetic optimization algorithm Computational intelligence Electric vehicle Electric vehicle charging station Meta-heuristics optimization

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Moisture content of Panax notoginseng taproot predicted using near infrared spectroscopy

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JOURNAL OF NEAR INFRARED SPECTROSCOPY 2024年第3期32卷 81-92页

作者： Zhang, Fujie Li, Shanshan Shi, Lei Li, Lixia Cui, Xiuming Kunming Univ Sci & Technol Fac Modern Agr Engn Kunming 650500 Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Sustainable Utilizat Panax Notogins Kunming Peoples R China

The rapid determination of moisture content in Panax notoginseng taproot (PNT) was determined using a portable near infrared spectrometer (900 similar to 1700 nm). First, to reduce baseline offset of the spectra Savitzky-Golay and standard normal variate transformation were combined to preprocess the original spectral data. Then, competitive adaptive reweighting sampling and bootstrapping soft shrinkage (BOSS) were employed to extract feature wavelengths that could characterize the moisture content information of PNT respectively. Finally, the least square support vector regression (LSSVR) model was established based on feature spectra and full spectra. To improve the prediction accuracy of the model, a LSSVR model based on the arithmetic optimization algorithm (AOA) was proposed, and the optimization results were compared with those of the snake optimizer and particle swarm optimization. The results indicated that the best prediction model was BOSS-AOA-LSSVR, with r(2) and RMSEP values of 0.96 and 0.03%, respectively. Thus, it is feasible to predict the moisture content of Panax notoginseng taproot by portable near infrared spectroscopy in combination with BOSS-AOA-LSSVR. The results show that portable near infrared spectroscopy can be used to predict the moisture content of Panax notoginseng taproot, which provides a theoretical basis for the rapid and non-destructive detection of the moisture content of Panax notoginseng taproots.

关键词： Panax notoginseng taproot moisture content portable near infrared spectroscopy arithmetic optimization algorithm least squares support vector regression

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A Multiscale Atrous Convolution-based Adaptive ResUNet3+with Attention-based ensemble convolution networks for brain tumour segmentation and classification using heuristic improvement

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 91卷

作者： Reddy, Baireddy Sreenivasa Sathish, Anchula Jawaharlal Nehru Technol Univ Anantapur Ananthapuramu Andhra Pradesh India Govt Polytech Vempalli YSR Kadapa Andhra Pradesh India RGM Coll Engn & Technol Nandyal Andhra Pradesh India

A brain tumor is a tissue group formed by the addition of unusual cells in the brain, and it's significant to identify brain tumor through Magnetic Resonance Imaging (MRI) for treatment. Because of the different tumor types, better segmentation of brain tumor is a tricky problem in MRI. To conquer the existing issues, the latest scheme is proposed for brain tumor classification and segmentation by deep structured architectures. In the first stage, the source images are garnered from online data sources. The garnered data is then subjected to a new segmentation model which is termed as Multiscale Atrous Convolution-based Adaptive ResUNet3+ (MAC-ARUNet3 + ). Here the parameters of deep learning approaches are optimally tuned by Improved Math Optimizer Accelerated-based arithmetic optimization algorithm (IMOA-AOA). Finally, the tumor classification is carried out by Attentionbased Ensemble Convolution Networks (AECN), in which it is encompassed with ResNet, Inception, and MobileNet. Thus, the final results are determined by the high-ranking-based estimation. The performance and comparative analysis are done using diverse measures and distinct algorithms. Hence, the recommended model outperforms with the high segmentation and classification accuracy that aids the practitioner in diagnosing the disease rapidly.

关键词： Magnetic Resonance Imaging Brain Tumor Segmentation And classification Multiscale Atrous Convolution based Adaptive ResUNet3+ Improved Math Optimizer Accelerated-based arithmetic optimization algorithm Attention-based Ensemble Convolution Networks

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A novel hybrid method for modeling of photovoltaic module I-V characteristic curve by using artificial intelligence-based solver and multi-criteria decision making

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ENERGY EXPLORATION & EXPLOITATION 2024年第1期42卷 309-329页

作者： Almunem, Ruqayah Dheyauldeen A. Muhsen, Dhiaa Halboot Haider, Haider Tarish Khatib, Tamer Mustansiriyah Univ Dept Comp Engn Baghdad Iraq Annajah Natl Univ Dept Energy Engn & Environm Nablus Palestine

In this research, hybrid method is proposed to model the I-V characteristic curve of a photovoltaic (PV) module. The method is represented by a multi-objective arithmetic optimization and cuckoo search with multi-criteria decision-making approach. The proposed model generates first a number of I-V curves as candidates. This phase is conducted through multi-objective optimization algorithm. The optimization algorithm is assessed by a non-dominated ranking scheme and crowding distance framework. After that, the best I-V curve candidate is chosen from the result of Pareto front by using the VIKOR multi-criteria decision-making method. Moreover, the analytic hierarchy approach is employed to select the appropriate weight for each criterion. The proposed method is validated by using an experimental data under various operational conditions. This validation is done by extracting different I-V characteristic for PV modules. The proposed method is compared to a number of methods in the literature. Results show that the proposed method exceeds other methods in the literature considering the accuracy of generating the I-V curves. In addition, results show that the proposed method requires less computational power as compared to other hybridized methods.

关键词： Single diode photovoltaic module I-V curve arithmetic optimization algorithm cuckoo search

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Intrusion Detection System with an Ensemble Learning and Feature Selection Framework for IoT Networks

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IETE JOURNAL OF RESEARCH 2023年第12期69卷 8859-8875页

作者： Rohini, G. Gnana Kousalya, C. Bino, J. St Josephs Inst Technol Dept ECE Chennai 600119 Tamil Nadu India

The Internet of Things (IoT) and its applications are currently the most popular research areas. The properties of IoT are easily adapted to real-life applications but they disclose threats. In computer security, the Intrusion Detection System (IDS) plays an essential role in identifying and repealing malicious deeds in computer networks. The main purpose of this work is motivated by IoT security enhancement for IDS development using ensemble learning and proposing suitable methods for classifier performance. Initially, the preprocessing strategy is used for data cleaning, encoding and normalization, which are conducted in the RPL-NIDDS17 dataset. After that, the Synthetic Minority Oversampling Technique (SMOTE) is used to balance the dataset. Secondly, the Convolution Neural Network (CNN) has been used to extract the features from the dataset. From the extracted features, the optimal features are selected by the proposed arithmetic optimization algorithm (AOA). Finally, it is applied to the proposed weighted majority voting classifier. The AOA with the Butterfly optimization algorithm (BOA) is utilized to integrate the predictions of different classifiers to select the most vote class. This enhances the chances of perceived RF, kNN, SVM kernel, Bi-LSTM and GRU classifiers. The proposed method experiment is conducted in the MATLAB platform with the RPL-NIDDS17 dataset. The proposed scheme shows better performances in terms of accuracy, error, sensitivity, specificity, FPR, F1_score, Kappa and MCC, which are compared with the existing methods and algorithms.

关键词： arithmetic optimization algorithm Butterfly optimization algorithm ensemble classifier Internet of Things intrusion detection system RPL-NIDDS17

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Implementation of optimized extreme learning machine-based energy storage scheme for grid connected photovoltaic system

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JOURNAL OF ENERGY STORAGE 2024年 88卷

作者： Ganesh, Reddi Saha, Tapas Kumar Kumar, M. L. S. Sai Natl Inst Technol Durgapur Dept Elect Engn Durgapur 713209 West Bengal India Vignans Inst Informat Technol Dept Elect & Elect Engn Visakhapatnam 530049 Andhra Pradesh India

The intermittency of solar energy directly aggravates the imbalance between the generation and load demand of conventional power system, thereby increasing the stress on the grid under peak load conditions. The irradiance, temperature as well as energy generation varies with time. Forecasting of photovoltaic (PV) energy generation helps to plan the charging-discharging decision of the energy storage systems to reduce imbalance between the generation and load demand. Therefore, an optimized extreme learning machine (ELM) is proposed in this work for an online short-term forecast of the PV generation. The forecasted power along with measured power is used to generate a strategy for storing or emptying the storage system. The proposed scheme generates the reference current for DC-DC bidirectional converter that control energy in storage system. The forecastingbased energy management for grid connected microgrid system is implemented using MATLAB/Simulink considering its dynamics. Practical irradiance, temperature data obtained from real PV system are used for validating and testing the proposed technique. The results show that the proposed technique is able to provide robust energy storage scheme for microgrid along with limited time of power exchange between the utility grid with the varied intermittence of irradiance, temperature under the variation of AC load demand.

关键词： Extreme learning machine Energy storage system State of charge Artificial intelligence arithmetic optimization algorithm

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Hybrid Deep Learning Model and Fuzzy C Means Clustering Method for Pulmonary Nodule Detection in CT Images

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IETE JOURNAL OF RESEARCH 2023年第11期69卷 7993-8005页

作者： Amsaveni, D. Malleswaran, M. Anna Univ Ranippettai Engn Coll Dept ECE Wlaljapet 632513 Tamil Nadu India Anna Univ Univ Coll Engn Kancheepuram Dept ECE Kancheepuram 631552 Tamil Nadu India

Lung cancer is a disease caused by a spontaneous proliferation of cells in the lung tissue. It is very important to detect cancer cells in the lungs that release oxygen to the human body and carbon dioxide in the body as a result of vital functions. Hence, in this paper, Novel Hybrid Deep Learning Model (HDLM) and Fuzzy C Means Clustering Method (FCM) are developed for Pulmonary Nodule Detection in CT Images. The HDLM is a combination of a Deep Neural Network (DNN) and an arithmetic optimization algorithm (AOA). In the HDLM, the weight parameters are selected with the help of the AOA algorithm. The proposed methodology is working based on four phases: pre-processing phase, candidate nodule detection phase, feature extraction phase, and classification phase. In the pre-processing phase, the median filter is utilized to remove unwanted features from the input image. In the candidate module detection phase, FCM is utilized for reducing computational complexity and speeding up nodule detection. In the feature extraction phase, intensity-based features are utilized such as first-order statistics and second-order statistics. The first-order statistics are considered various features such as mean, variance, entropy, energy, and skewness. The second-order statistics are considered as gray level co-occurrence matrix (GLCM) features. At last, the HDLM is utilized in the classification phase which is used to reduce the false-positive result and improve the overall sensitivity of the system. The proposed methodology is evaluated by performance metrics and compared with conventional methods.

关键词： arithmetic optimization algorithm Candidate nodule detection Deep neural network Feature extraction phase and classification phase Lung cancer detection Pre-processing phase

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Soft Sensor Modeling of Recursive Interval Type-2 Fuzzy Neural Network Based on Logarithmic t-Norm

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2024年第6期35卷 1161-1176页

作者： Liu, Fan Zhao, Taoyan Cao, Jiangtao Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Peoples R China

Addressing the challenges posed by high complexity, ambiguity in model structure determination, and overfitting during parameter learning in the soft sensor modeling of intricate industrial processes, this study introduces a recursive interval type-2 fuzzy neural network utilizing a logarithmic (log) t-norm (RIT2FNN-log). Unlike the conventional interval type-2 fuzzy neural network that employs the product t-norm, which often results in insufficient excitation intensity and inaccurate numerical calculations, the proposed logarithmic t-norm enhances excitation intensity and magnifies subtle differences, thereby enhancing network accuracy. The optimization of RIT2FNN-log involves two main stages: structural learning and parameter learning. Structural learning employs an arithmetic optimization algorithm with mean square error as the fitness function to determine the optimal number of rules and initial parameter values for RIT2FNN-log. Parameter learning utilizes a hybrid learning algorithm, with the antecedent parameters trained using a gradient descent algorithm based on AdaBound to prevent overfitting, and the consequent parameters trained using the least squares method based on recursive singular value decomposition for rapid model convergence. The efficacy of RIT2FNN-log was demonstrated through applications in modeling tasks combined cycle power plant full-load output power prediction and Box-Jenkins time series analysis. Experimental comparisons with existing models revealed superior performance of RIT2FNN-log in terms of simplicity in network structure and lower mean square error.

关键词： Interval type-2 fuzzy neural network Logarithmic t-norm arithmetic optimization algorithm Recursive singular value decomposition least squares method Combined cycle power plant full-load output power

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Student performance prediction employing k-Nearest Neighbor Classification model and meta-heuristic algorithms

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第4期7卷 4397-4412页

作者： Song, Xiaohuan Shaanxi Normal Univ Fac Educ Xian 710000 Shaanxi Peoples R China Northwest Univ Basic Course Dept Modern Coll Xian 710000 Shaanxi Peoples R China

The precision of predicting student performance in Mathematics is pivotal for educational advancement, relying heavily on advanced machine learning (ML) methodologies. This predictive approach involves analyzing comprehensive datasets, emphasizing academic records, demographics, and various educational metrics, with a particular focus on Mathematics. Techniques like classification, regression analysis, decision trees, and neural networks yield highly accurate projections, aiding in timely interventions crucial for supporting students navigating mathematical complexities. These algorithms optimize resource allocation within educational institutions, identifying and aiding students requiring extra assistance in their mathematical pursuits. This study pioneers the enhancement of predictive capabilities in Mathematics through the innovative integration of the K-Nearest Neighbor Classification (KNNC) model with 2 novel optimization techniques: the Honey Badger algorithm (HBA) and the arithmetic optimization algorithm (AOA). By harnessing these cutting-edge ML and bio-inspired algorithms, the research is dedicated to pushing the boundaries of precision and reliability in forecasting, with a specific focus on elevating educational outcomes within the domain of Mathematics. The outcomes obtained for G1 and G3 reveal that the KNHB model exhibited outstanding performance in predicting and categorizing G3. It achieved remarkable Accuracy and Precision of 0.921 and 0.92, respectively. Moreover, the KNHB proved to be the most precise predictor for G1 value prediction, with Accuracy and Precision scores of 0.899% and 0.9%, respectively, in the prediction task.

关键词： Mathematics Classification Student performance K-Nearest Neighbor Classification Honey Badger algorithm arithmetic optimization algorithm

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