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Journal of Network Intelligence 2024年第1期9卷 613-628页

作者： Sitenda, Amos Song, Pei-Cheng Chu, Shu-Chuan Chen, Shi-Huang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Computer Science and Information Engineering Shu-Te UniversityKaohsiung Kaohsiung824 Taiwan

Differential Evolution (DE) is a potent stochastic evolutionary optimization algorithm garnering increasing research attention. Over the years, it has been found applicable in solving diverse real-world problems. DE employs mutation, crossover, and selection operators to guide populations toward optimal or nearly optimal solutions. However, the standard DE mutation strategies have found limitations in balancing exploration and exploitation effectively, thus prompting research into possible improvements. This study introduces a novel mutation strategy named Chebyshev Infused Chaos Mutation Strategy (CICMS). where a chaotic sequence partially guides the process of donor vector generation. Rigorous evaluations were conducted, comparing our modified DE against the standard DE and seven other metaheuristic algorithms, including Genetic Algorithm, Particle Swarm Optimization and Gravitational Search Algorithm. Experiments were performed using the challenging CEC 2014 benchmark functions, consisting of 30 objective functions. Results indicate substantial improvements in convergence speed and solution quality, highlighting the potential of our novel mutation strategy to enhance DE’s practicality in addressing complex optimization problems. This research contributes valuable insights to the dynamic field of optimization algorithms with implications for a wide range of applications. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Chaos theory

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Finite-difference time-domain methods

NATURE REVIEWS METHODS PRIMERS

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NATURE REVIEWS METHODS PRIMERS 2023年第1期3卷 1-19页

作者： [Anonymous] Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Department of Electrical and Computer Engineering University of Utah Salt Lake City UT USA Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Department of Electrical and Electronic Engineering The University of Manchester Manchester UK Biomedical Engineering Department Northwestern University Evanston IL USA Department of Electrical and Software Engineering University of Calgary Calgary Canada Department of Electrical and Computer Engineering Purdue University West Lafayette IN USA

This PrimeView highlights how algorithm and hardware develops could expand the application range of finite-difference time-domain methods.

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An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

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Multimedia Tools and Applications 2025年第4期84卷 1697-1724页

作者： Shukla, Preeti Chandanan, Amit Kumar Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India Department of Computer Science & Engineering Guru Ghasidas Vishwavidyalaya CG Bilaspur India

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2

关键词： Long short-term memory

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Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

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Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

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Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

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computer Modeling in engineering & sciences 2023年第5期135卷 1275-1294页

作者： Byoungwook Kim Hong-Jun Jang Department of Computer Engineering Dongshin UniversityNaju58245Korea Department of Computer Science and Engineering Jeonju UniversityJeonju55069Korea

As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is *** of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical *** this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN *** existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the *** solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each *** order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in *** conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force *** experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 ***-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).

关键词： Spatial clustering DBSCAN algorithm genetic algorithm textual information

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Efficient 3D Tracking and Detection of Multi-Rotor UAVs Using mmWave Radar with Semi-Supervised Learning

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IEEE Sensors Journal 2025年第12期25卷 22001-22014页

作者： Xi, Rui Wei, Wenjie Zhang, Malu University of Electronic Science and Technology of China Department of Computer Science and Engineering Chengdu611731 China

Small UAVs pose security risks to sensitive areas and individuals due to their rapid movement and wide coverage capabilities. Effective monitoring necessitates the deployment of lightweight and energy-efficient surveillance systems. This research introduces an efficient 3D tracking and detection approach for small UAVs, utilizing millimeter-wave radars and Spiking Neural Networks (SNNs). By capturing micro-Doppler characteristics of UAV movements, it effectively processes low signal-to-noise ratios and uncertain signals. An improved angle estimation algorithm, combining dynamic programming and particle filters, enables realtime 3D UAV tracking with reduced computational complexity. Then, a simple UAV detection model based on SNN architecture is developed by leveraging UAVs’ position and corresponding Doppler information. Furthermore, a bio-inspired semi-supervised method is proposed to facilitate the training of SNNs using a limited number of annotated samples. The effectiveness of the proposed methodology is evaluated under various environmental conditions. Results indicate a significant improvement in tracking computation time efficiency, with the recognition model size reduced to one-tenth of its original size, yet it maintains near-original system performance. © 2001-2012 IEEE.

关键词： Semi-supervised learning

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Optimal Design of a Planning and Contracting Framework to Enable Vehicle to Building and Grid Services via Demand Response

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IEEE Transactions on Transportation Electrification 2025年第3期11卷 7542-7556页

作者： Abd Elaziz Elsayed, Ahmed Saxena, Shivam Essa Zidan Farag, Hany York University Lassonde School of Engineering Electrical Engineering and Computer Science Department Keele Campus TorontoONM3J 1P3 Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B5A3 Canada

Bidirectional electric vehicle (EV) charging enables stored energy to reduce peak loads for vehicle to buildings (V2Bs) and the vehicle to grid (V2G). However, building owners investing in V2B infrastructure while generating revenue from V2G services face challenges in planning and coordinating with EV owners due to uncertainties in their schedules and profit-sharing expectations. Additionally, misaligned building and grid peak times can create conflicts between V2B and V2G goals, which may negatively impact the building electricity bill. Unlike previous studies that used a contract-free approach for aggregating V2B and V2G, resulting in inconsistent participation, this article proposes a novel planning and contracting framework that enables building owner to determine the optimal contract parameters with EV owners. These parameters include minimum participation time in DR events, minimum arrival state of charge (SoC), and permitted emergency departure hours. The framework supports V2B aggregation with on-site distributed energy resources (DERs) for DR and V2G services, ensuring transparency and fairness through shared profits and performance-based penalties, while compensating building electricity bills due to V2G activities. The framework is composed of a tri-stage optimization process that uses Monte Carlo simulations to generate EV owner profit assessments, select optimal EV candidates based on charger availability, estimate contract parameters with profit/penalty sharing, and assign contracts between power system operators (PSOs), building owners, and EV owners under multiple virtual scenarios. Simulation results validate the contract parameter assessment and its performance via a 3-day case study with real-world datasets that demonstrates net revenue generation of {\} 209 for each EV owner and {\} 950 for building owners during DR events with a return on investment (ROI) of 134.5% and 130.7% for the EVs and Building owners, respectively. © 2015 IEEE.

关键词： Vehicle-to-grid

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Energy-Efficient and Robust Communication in Multi-Connectivity Enabled mmWave Ultra-Dense Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年第2期9卷 621-634页

作者： Kim, Jungjun Lee, Jihwan Bahk, Saewoong Seoul National University Department of Electrical and Computer Engineering INMC Seoul08826 Korea Republic of

The ultra-dense network (UDN) concept with multi-connectivity (MC) has emerged as a promising scenario for millimeter-wave (mmWave) communications due to its synergistic effect. However, mmWave UDNs with MC face challenges such as escalated energy consumption and computational complexity for traffic splitting decisions. To save energy and reduce the need for frequent traffic splitting decisions in the considered scenario, we formulate the problem as a mixed integer programming (MIP). Our objective is to determine which base stations (BSs) to enable for energy saving and how to split user traffic to maintain robust communication in MC enabled UDNs. To this end, this paper proposes energy-efficient and robust communication (EERC), an ensemble of heuristic methods and convex optimization, to achieve our goal without directly solving the computationally expensive MIP. In addition, we consider greedy algorithm that potentially offers enhanced optimality. We analyze the complexity of each scheme and demonstrate that EERC is the most viable option considering both performance and complexity. Through extensive simulations, we demonstrate that EERC achieves over 98% optimality for small-sized networks. More importantly, we evaluate EERC in realistic scenarios using a 3-D ray-tracing simulator and confirm that EERC performs very well even in urban terrain environments with buildings. © 2017 IEEE.

关键词： Convex optimization

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A high-capacity reversible data hiding with contrast enhancement and brightness preservation for medical images

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Multimedia Tools and Applications 2025年第8期84卷 5239-5264页

作者： Gandhi, Sonal Kumar, Rajeev Department of Computer Science and Engineering Delhi Technological University Delhi110042 India

The healthcare industry has witnessed an increase in the use of cloud storage, resulting in a significant demand for safeguarding medical records from potential attackers. In response to this challenge, reversible data hiding (RDH) has emerged as a lifesaver. The RDH ensures the concealment of private and confidential data with minimal loss to the cover image while offering opportunities for image enhancement. This paper introduces a new RDH method with contrast enhancement for medical images. The method aims to provide high embedding capacity (EC) while preserving brightness. To achieve this, the proposed method initially segments cover images into regions of interest (ROI) and non-regions of interest (NROI) and employs different embedding strategies based on the characteristics of each region, thereby enhancing embedding performance. Furthermore, a novel pre-processing technique is introduced for processing ROI pixels. This technique re-organizes and creates empty bins to provide enlarged EC with less distortion by capitalizing on the unique properties of medical images. Additionally, the proposed method embeds secret data in the NROI to further increase the EC. Experimental results show that the proposed method effectively preserves brightness (with 0.99 Relative Mean Brightness Error) while providing higher EC, exceeding the best-known methods by 20%. Furthermore, the method enhances contrast with minimal distortion in the output images. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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Novel methodology for apple leaf disease classification with PCNN-IELM

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Neural Computing and Applications 2025年第6期37卷 4895-4913页

作者： Navpreet Roul, Rajendra Kumar Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala147004 India

Agriculture is crucial to the global economy, particularly in ensuring food security. Recent trends indicate that various plant diseases are causing substantial financial losses in the agricultural sector worldwide. Traditional manual inspection methods for detecting fruit and plant diseases are labor-intensive and inefficient. Adopting automated disease detection technologies could significantly enhance early diagnosis and reduce the economic impact of these diseases on agriculture. This study introduces an advanced model for classifying apple diseases by integrating a pre-trained convolutional neural network (PCNN), such as VGG16, VGG19, or ResNet50, with an incremental extreme learning machine (I-ELM) for efficient feature extraction and classification. A key innovation of this model is replacing the PCNN’s fully connected layer with the I-ELM, which eliminates the lengthy back-propagation process and significantly reduces training time. Integrating I-ELM with PCNN harnesses the rapid learning capabilities and robust generalization of I-ELM with the superior feature extraction abilities of CNNs. I-ELM simplifies the network architecture by avoiding the complex neural networks commonly used in other methods. The model’s effectiveness is rigorously evaluated on the well-known Plant Village dataset, demonstrating its ability to identify various apple diseases through performance metrics such as precision, sensitivity, specificity, accuracy, and the F1-score. Comparing existing deep learning models using these metrics highlights its superior performance. This innovation is up-and-coming for intelligent agricultural systems, offering an effective solution for classifying apple diseases and enabling timely and innovative farming practices. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fruits

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