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Augmented electric eel foraging optimization algorithm for feature selection with high-dimensional biological and medical diagnosis

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Neural Computing and Applications 2024年第35期36卷 22171-22221页

作者： Al-Betar, Mohammed Azmi Braik, Malik Sh. Mohamed, Elfadil A. Awadallah, Mohammed A. Nasor, Mohamed College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Science Al-Balqa Applied University Al-Salt19117 Jordan Department of Computer Science Al-Aqsa University P.O. Box 4051 Gaza Palestine Ajman University Ajman United Arab Emirates

This paper explores the importance of the electric eel foraging optimization (EEFO) algorithm in addressing feature selection (FS) problems, with the aim of ameliorating the practical benefit of FS in real-world applications. The use of EEFO to solve FS problems props our goal of providing clean and useful datasets that provide robust effectiveness for use in classification and clustering tasks. High-dimensional feature selection problems (HFSPs) are more common nowadays yet intricate where they contain a large number of features. Hence, the vast number of features in them should be carefully selected in order to determine the optimal subset of features. As the basic EEFO algorithm experiences premature convergence, there is a need to enhance its global and local search capabilities when applied in the field of FS. In order to tackle such issues, a binary augmented EEFO (BAEEFO) algorithm was developed and proposed for HFSPs. The following strategies were integrated into the mathematical model of the original EEFO algorithm to create BAEEFO: (1) resting behavior with nonlinear coefficient;(2) weight coefficient and confidence effect in the hunting process;(3) spiral search strategy;and (4) Gaussian mutation and random perturbations when the algorithm update is stagnant. Experimental findings confirm the effectiveness of the proposed BAEEFO method on 23 HFSPs gathered from the UCI repository, recording up to a 10% accuracy increment over the basic BEEFO algorithm. In most test cases, BAEEFO outperformed its competitors in classification accuracy rates and outperformed BEEFO in 90% of the datasets used. Thereby, BAEEFO has demonstrated strong competitiveness in terms of fitness scores and classification accuracy. When compared to its competitors, BAEEFO produced superior reduction rates with the fewest number of features selected. The findings in this research underscore the critical need for FS to combat the curse of dimensionality concerns and find highly useful fea

关键词： Heuristic algorithms

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Hybrid Power Generation Forecasting Using an Intellectual Evolutionary Energy-Preserve Rate Clustering Technique 1

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Webber, Julian L. Jayagopal, Vellingiri Mehbodniya, Abolfazl Sengan, Sudhakar Velayutham, Priya Rangasamy, Rajasekar Stalin David, D. Doha Kuwait Department of Software & System Engineering School of Information Technology & Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai600072 India

ISBN: (数字)9789811974557

ISBN: (纸本)9789811974540

The significant growth of Wind Power (WP) has significant challenges due to the inconsistency and uncertainty of Wind Power Generation (WPG) to ensure the power system. Solar energy and WP calculation are great ways to overcome the problem. Mainly voltage and frequency fluctuations and harmonics have a high impact on the power generation and distribution system;in any high case, the power quality of grid systems is reduced. This can be primarily solved with proper design, advanced rapid response control facilities and optimization of power systems. A new type of Multi-term Wind Energy (WE) with an IEEE 30 bus forecasting system based on digital weather forecasting and error modification methods is presented. From the Evolutionary Energy- Preserve Rate Clustering (EEPRC) Technique-based technique for the evaluates the probability ratio in Solar and wind error energy prediction, the error assessment model is proposed, which has the advantage of estimating from real wind farms and statistical Weather Forecast (WF) data and has proven to be a nominal method for improving the predictive accuracy of Multi-term WPG. The primary step of the Energy-Preserve Rate Clustering (EPRC) method is to calculate the uncertainty of real-time data with the predicted value. The computed values are helpful for the increases in Solar WPG production. The proposed wind forecasting system is utilized in the Coastal area-based energy generation. Also, it has been demonstrated that wind speed and WPG with its probability ratio have been evaluated with the EEPRC-based forecasting method, especially for its powerful predictive capability for the complex properties of the controller function. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Detection of Medicinal Plants Using Machine Learning 1

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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A Virtual Network Mapping Algorithm Based on Time

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Chinese Journal of Electronics 2025年第1期23卷 31-36页

作者： Ming Jiang Zhiyang Zhao Min Zhang Jingfan Tang Chunming Wu Xiao Min Institute of Software and Intelligent Technology Hangzhou Dianzi University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Mathematics and Information Engineering College of Jiaxing University Jiaxing China

As one of the challenges for network virtualization, virtual network embedding which maps Virtual network (VN) to the substrate network and allocates resources according to the requirements of VN in an efficient way has gained great attention. Existing algorithms generally make their decision according to the present available substrate network resource, especially bandwidth. This paper proposes a time-based VN embedding algorithm. A probability model is formulated to obtain the maximum probability that the available resources of substrate network can be used by succeeding VN requests. The probability is set as the weight of the node and the greedy algorithm is employed to embed the virtual node. The reciprocal of the probability is set as the weight of the link and the shortest path algorithm is employed to embed the virtual link. Simulation experiments show that the proposed algorithm increases the acceptance rate and the revenue compared to the existing algorithms.

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An Initial Solution Based Selective Harmonic Elimination Method for Multilevel Inverter

CPSS Transactions on Power Electronics and Applications

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CPSS Transactions on Power Electronics and Applications 2024年第1期9卷 63-78页

作者： Kala, Peeyush Sharma, Abhinav Jately, Vibhu Joshi, Jyoti Yang, Yongheng Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Ghaziabad201204 India Department of Electrical and Electronics Engineering Dehradun248007 India Dehradun248007 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun248002 India College of Electrical Engineering Zhejiang University Hangzhou310027 China

This article presents an initial solution based selective harmonic elimination (SHE) method for multilevel inverter (MLI) that aims to solve SHE problem with high accuracy while significantly reducing the number of iterations. Initial SHE solution is defined as a set of initial switching angles that has less accuracy as compared to the final SHE solution. To find initial SHE solution, tunicate swarm algorithm (TSA), grey wolf optimization (GWO) and whale optimization (WO) are used to solve five-dimensional SHE problem. A comparative analysis demonstrates that TSA achieved the desired initial SHE solution within lesser iterations as compared to GWO and WO for a wide range of modulation indices. The initial switching angles found using TSA are further optimized using Newton-Raphson (NR) method being the proposed TSANR method, which exploits the SHE search space with quadratic rate of convergence to find final SHE solution. The proposed TSANR method significantly minimizes the computational time and further improves the accuracy of the SHE solution as compared to the state-of-the-art SHE methods. Detailed simulations, experimental and harmonic analysis under dynamic change in modulation index are presented to show the efficacy of the proposed SHE method in terms of control over fundamental and detrimental harmonics. © 2017 CPSS.

关键词： Harmonic analysis

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Multi-label and Multi-class Classification on a Custom Dataset using Convolution Neural Networks 7

Multi-label and Multi-class Classification on a Custom Datas...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Bonthu, Sridevi Kiran, Kompella Bhargava Deenakonda, Mamatha Rao, V.V.R. Maheswara Jagadeesh, S.V.V.D. Vishnu Institute of Technology Computer Science and Engineering Bhimavaram India Vishnu Institute of Technology Electrical and Electronics Enigneering Bhimavaram India Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India

ISBN: (纸本)9798350397253

A multi-label image classification is a challenging task as it has to map an input image to a vector of outputs. This work presents a single and efficient model to perform multi-label and multi-class classification using Convolutional Neural Networks (CNN). A custom dataset is collected and annotated with eight labels to test the neural network. The classes for the labels in the dataset are a blend of balanced and unbalanced data. The proposed network is a two-stage network. The first stage is used for general training, whereas the second stage is used for specific training. The results show that the usage of a single model across multiple labels results in varying performance. The number of classes and the balance in the class distribution have a direct impact on the performance of the model. The code is available at https://***/sridevibonthu/FML. © 2023 IEEE.

关键词： Convolutional neural networks

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Detection of Stage of Cancer Using Machine Learning and Deep Learning 5th

Detection of Stage of Cancer Using Machine Learning and Deep...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Ashtagi, Rashmi Akkalkot, Afsha Imran Kotecha, Ketan Maginmani, Umme Habiba Patil, Pushpalata Patil, Abhijeet Computer Engineering Department Vishwakarma Institute of Technology Pune India Computer Engineering Department College of Engineering Pune Technological University Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India Computer Science and Engineering SECAB Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Vijayapur India Computer Science and Engineering Faculty of Engineering and Technology Exclusively for Women Sharnbasva University Kalburagi India RPD PU College of Arts and Commerce Belagavi India

ISBN: (纸本)9789819761029

Melanoma, an aggressive form of skin cancer known for its rapid spread, poses a significant threat and is responsible for a substantial number of fatalities. The accurate classification of cancer stages is crucial for effective diagnosis, particularly during surgical treatment. This paper introduces two methods for categorizing melanoma cancer stages. Initial system distinguishes between stage 1 and stage 2, while the second system extends the classification to include stage 3 melanoma. Leveraging a Convolutional Neural Network (CNN) algorithm, the proposed system incorporates the Similarity Measure for Text Processing (SMTP) as a loss function. The experimental results showcase the efficacy of different loss functions, with a particular focus on the proposed SMTP loss function, which outperforms several others designed for classification problems. This algorithm stands out as a more efficient approach for melanoma stage classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Simplifying Digital Streaming: An Innovative Cross-Platform Streaming Solution 15

Simplifying Digital Streaming: An Innovative Cross-Platform ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Muduli, Debendra Sahoo, Saroj Kumar Mishra, Jayanta Kumar Pradhan, Debasish Sharma, Santosh Kumar C.V Raman Global University Dept. of Computer Science Engineering Odisha Bhubaneswar India Centurion University of Technology and Management Dept. of Computer Science Engineering India GITA Autonomous College Dept. of Computer Science Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

Our research aims to investigate how reinforcement learning methods can be applied to develop a movie recommendation system. We frame the issue of interactive recommendation as a contextual multi-armed bandit, where the system learns user preferences by suggesting new movies and receiving feedback. Through reinforcement learning, we address challenges such as balancing exploitation and exploration and tackling the coldstart problem. Additionally, we incorporate the novelty factor of movies into our model. We evaluate both content-based and collaborative filtering approaches which achieved accuracy 72.33(%), both of which demonstrate effective recommendation outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Ensemble Learning: Importance, Applications, Challenges, and Future Work

Ensemble Learning: Importance, Applications, Challenges, and...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Manjunathan, N. Shanmugapriya, D. Girija, P. Vasudevan, M. Murugesan, S. Siva Subramanian, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India IFET College of Engineering Department of Artificial Intelligence and Machine Learning Tamilnadu Villupuram India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept of CSE India Andhra Pradesh Madanapalle517325 India R.M.D Engineering College Department of Computer Science and Engineering Kavaraipettai India RMK College of Engg and Tech Department of Computer Science and Engineering India

ISBN: (纸本)9798331541170

Ensemble learning has become one of the most used approaches in machine learning that helps to increase predictive capabilities and model stability. Thus, this survey paper offers an extensive analysis of ensemble learning, stressing its significance, potential uses, inherent difficulties, and future research prospects. In the first step, we explain the basic ideas and the major ensemble approaches, including bagging, boosting, and stacking. We then discuss the flexibility of ensemble learning in classification, regression, and outlier detection problems and its implications in health informatics, finances, e-business, and self-driving systems. However, there are challenges associated with ensemble learning, for example, computational cost, the quality of data used, and the fact that the models themselves cannot be explained. These are the issues that can hinder the performance of ensemble models and the ideas to help to improve the efficiency of this method. In addition, we outline potential future research avenues such as novel ensemble learning methods, optimization of computational complexity, interpretability of models as well as new use cases in emerging fields like IoT, quantum computing, and edge computing. We also highlight the need to consider the ethical issues towards attaining fairness and accountability in ensemble learning. The results of this survey should contribute to raising the base of knowledge about ensemble learning and stimulate further research and development in this actively evolving area. © 2024 IEEE.

关键词： Federated learning

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Optimized Tuned Deep Learning Model for Chronic Kidney Disease Classification

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computers, Materials & Continua 2022年第2期70卷 2097-2111页

作者： R.H.Aswathy P.Suresh Mohamed Yacin Sikkandar S.Abdel-Khalek Hesham Alhumyani Rashid A.Saeed Romany F.Mansour Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

In recent times,Internet of Things(IoT)and Cloud Computing(CC)paradigms are commonly employed in different healthcare *** gadgets generate huge volumes of patient data in healthcare domain,which can be examined on cloud over the available storage and computation resources in mobile *** Kidney Disease(CKD)is one of the deadliest diseases that has high mortality rate across the *** current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm(FPA)-based Deep Neural Network(DNN)model abbreviated as *** steps involved in the presented FPA-DNN model are data collection,preprocessing,Feature Selection(FS),and ***,the IoT gadgets are utilized in the collection of a patient’s health *** proposed FPA-DNN model deploys Oppositional Crow Search(OCS)algorithm for FS,which selects the optimal subset of features from the preprocessed *** application of FPA helps in tuning the DNN parameters for better classification *** simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD *** results were examined under different *** simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%,specificity of 98.66%,accuracy of 98.75%,F-score of 99%,and kappa of 97.33%.

关键词： Deep learning chronic kidney disease IoT cloud computing feature selection classification

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