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An Approach in Melanoma Skin Cancer Segmentation With bat optimization algorithm

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第4期34卷

作者： Abed, Marwah Sameer Abed Akbas, Ayhan Cankiri Karatekin Univ Comp Engn Dept Cankiri Turkiye Univ Surrey Inst Commun Syst Guildford England

Numerous advancements and significant progress have been made in computer methods for medical applications, alongside technological developments. Automatic image analysis plays a crucial role in the realm of medical diagnosis and therapy. Recent breakthroughs, especially in the field of medical image processing, have enabled the automatic detection of various characteristics, alterations, diseases, and degenerative conditions using skin scans. Utilizing image processing methods, skin image analysis is instrumental in the identification and monitoring of conditions manifesting through alterations in skin structure. Notably, accurate segmentation of cancerous regions from the background remains a challenging task in the area of melanoma image analysis. The primary objective of this study is to achieve exceptional precision in delineating melanoma boundaries. Leveraging the bat optimization algorithm, we determine the optimal threshold for melanoma segmentation, effectively identifying the most accurate cancerous area boundaries. To evaluate the results, standard metrics such as accuracy, sensitivity, specificity, Dice coefficient, and F1 score are employed. In this study, we applied the bat optimization algorithm to determine the optimal threshold value for segmenting melanoma skin cancer, effectively identifying the most accurate cancerous area boundaries. For result evaluation, we employed standard metrics including accuracy, sensitivity, specificity, Dice coefficient, and F1 score, which yielded impressive values of 99.8%, 98.99%, 98.87%, 98.45%, and 98.24%, respectively.

关键词： accuracy metrics bat optimization algorithm image processing image segmentation medical image analysis melanoma skin cancer

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An improved bat optimization algorithm of Sports Video

An improved bat optimization algorithm of Sports Video

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2016 4th International Conference on Machinery,Materials and Computing Technology(ICMMCT 2016)

作者： Qingmei Liu Gansu Normal University for Nationalities

ISBN: (纸本)9781510821934

In this paper, an improved bat optimization algorithm to solve sports video was proposed, which presented the concept of adjustment sequence to design the strategy of local searching, and added the maneuver flight in the global exchange of information. Experimental results indicate that, the proposed algorithm has more powerful search capability and more strong robustness in solving sports video.

关键词： bat optimization algorithm Maneuver flight Sports Video intelligent algorithms Application

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An optimization of color halftone visual cryptography scheme based on bat algorithm

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JOURNAL OF INTELLIGENT SYSTEMS 2021年第1期30卷 816-835页

作者： Aswad, Firas Mohammed Salman, Ihsan Mostafa, Salama A. Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Ctr Intelligent & Autonomous Syst Dept Software Engn Johor Baharu 86400 Malaysia Univ Diyala Coll Basic Educ Comp Dept Diyala 32001 Iraq

Visual cryptography is a cryptographic technique that allows visual information to be encrypted so that the human optical system can perform the decryption without any cryptographic computation. The halftone visual cryptography scheme (HVCS) is a type of visual cryptography (VC) that encodes the secret image into halftone images to produce secure and meaningful shares. However, the HVC scheme has many unsolved problems, such as pixel expansion, low contrast, cross-interference problem, and difficulty in managing share images. This article aims to enhance the visual quality and avoid the problems of cross-interference and pixel expansion of the share images. It introduces a novel optimization of color halftone visual cryptography (OCHVC) scheme by using two proposed techniques: hash codebook and construction techniques. The new techniques distribute the information pixels of a secret image into a halftone cover image randomly based on a bat optimization algorithm. The results show that these techniques have enhanced security levels and make the proposed OCHVC scheme more robust against different attacks. The OCHVC scheme achieves mean squared error (MSE) of 95.0%, peak signal-to-noise ratio (PSNR) of 28.3%, normalized cross correlation (NCC) of 99.4%, and universal quality index (UQI) of 99.3% on average for the six shares. Subsequently, the experiment results based on image quality metrics show improvement in size, visual quality, and security for retrieved secret images and meaningful share images of the OCHVC scheme. Comparing the proposed OCHVC with some related works shows that the OCHVC scheme is more effective and secure.

关键词： visual cryptography halftone visual cryptography scheme error diffusion bat optimization algorithm

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Energy consumption assessment of Internet of Things (IoT) based on machine learning approach

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SIGNAL IMAGE AND VIDEO PROCESSING 2025年第5期19卷 1-23页

作者： Wang, Hui Dang, Zhizheng Hebei Chem & Pharmaceut Coll Shijiazhuang 050026 Hebei Peoples R China

Due to the fact that a huge amount of energy consumption takes place in today's city buildings, particularly in modern countries, this ought to be highlighted as one of the world's important issues, which will raise the requirement for developing a variety of evaluation methods so as to advance an optimal predictive device for consuming energies efficiently in buildings. On the one hand, Internet of Things (IoT) and its characteristics are the most popular research areas in real-life applications at present. On the other hand, machine learning (ML) techniques significantly has improved the Internet of things (IoT)'s capability to control energy consumption. To this end, this study, firstly, evaluated five models' performance in terms of predicting IoT-oriented energy consumption by dividing the studied dataset into 80% train and 20% test. The involved ML models were Adaptive Boosting, Histogram-based Gradient Boosting Machine (HistGBM), K-Nearest Neighbors, Light Gradient Boosting Machine, Extreme Gradient Boosting. The contrastive investigation of the applied models' evaluation metric criteria demonstrated the supremacy of HistGBM model before optimization process, with the highest R-2 and the lowest RMSE. For further investigation, we tuned the parameters of the abovementioned models with bat optimization algorithm (BOA) for IoT-based energy consumption forecast in city buildings. The results are then examined for the opted model's hyperparameters using the optimization techniques, obtaining the most accurate and reliable hybrid model. The results confirm that the proposed hybrid BOA-XGBoost approach significantly improves the efficiency of the ML methods' forecasting. In particular, the achieved highest R-2 values by 0.9999 and 0.9979, respectively as well as the lowest RMSE of 0.34 and 4.70 for both training and testing dataset in building energy consumption prediction proved that the hybrid BOA-XGBoost model outperform the other models. The spent testing time f

关键词： Internet of Things Machine learning Energy consumption bat optimization algorithm XGBoost

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An Intelligent optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic devices has sparked a strong desire to incorporate the Internet of Things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. The objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the bat-Salp Swarm optimization (BSSO). This paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. The implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. The use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. The findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： bat optimization algorithm Salp optimization algorithm Smart homes Speech recognition

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bat optimization Model for Electric Vehicle Route optimization Under Time-of-Use Electricity Pricing

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期131卷 1461-1473页

作者： Vani, B. Veena Kishan, Dharavath Ahmad, Md Waseem Reddy, B. Naresh Kumar Natl Inst Technol Karnataka Dept Elect & Elect Engn Surathkal 575025 India Natl Inst Technol Dept Elect & Commun Engn Tiruchirappalli 620015 Tamil Nadu India

In the framework of fuel reduction and energy conservation, the electric vehicles (EV's) has been identified as a promising option in contrast to fuel-driven vehicles. EV's battery limits to require visiting a greater number of times to the recharging stations, which must be viewed as in the route planning to keep away from inefficient vehicle routes with lengthy diversions. These problems have to consider, we propose an Efficient Electric Vehicle Route optimization with Time-of-Use Electricity Pricing using bat algorithm. Which can reduce the used vehicles as well as electricity-cost and total travel distance. Additionally, functional model and collective models are used to minimize the objectives: distance and cost. The computational assessment in light of the notable benchmarking test instances exhibits, proposed optimization algorithm electricity cost conservation on average 12.17% with Learnable Partheno-Genetic algorithm (Yang et al. in IEEE Trans Smart Grid 6:657-666, 2015) 8.45% with VNS/TS algorithm (Lin et al. in Trans Res Part-C 130:103285, 2021) and 5.15% with Mixed Integer Programming model (Ham and Park in IEEE Access 9:37220-37228, 2021).

关键词： Electric vehicles (EVs) bat optimization algorithm Time-of-use Electricity price Electric vehicle routing problem

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Profit maximization in reverse logistics based on disassembly scheduling using hybrid bee colony and bat optimization

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TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2019年第4期43卷 551-559页

作者： Sathish, T. SMR East Coast Coll Engn & Technol Dept Mech Engn Pattukkottai 614612 Tamil Nadu India

This work was undertaken to develop a strategy to enhance the profit in the reverse logistics of end-of-life products. In this paper, a novel strategy based on hybrid bee colony and bat optimization technique is presented to perform reverse logistics. The aim of the present paper is to maximize the profit in reverse logistic based manufacturing. The proposed optimization technique was used to schedule the disassembly of end-of-life products so that the time spent in reverse logistics was reduced. Moreover, the proposed technique increases the amount of product required during disassembly, so that the loss of component is reduced. Thus, the proposed technique can enhance the manufacturer's profit by reducing the time and cost required for disassembly. Ultimately, the proposed technique can provide a suitable technique for multi-period disassembly in manufacturing industries.

关键词： end-of-life artificial bee colony bat optimization algorithm adaptive genetic algorithm hybrid bee colony

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Optimal Dispatch of Vehicle-to-Grid (V2G) battery Storage Using p-ELECTRE Method and Its Impact on Optimal Scheduling of DGs in Distribution System

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2019年第14-15期47卷 1362-1374页

作者： Yammani, Chandrasekhar Prabhat, Pankaj Dahal, Keshav Natl Inst Technol Dept Elect Engn Warangal Andhra Pradesh India Univ West Scotland Sch Engn & Comp Paisley Renfrew Scotland

In recent past, the number of electric vehicles (EVs) have increased significantly due to their various advantages to environment. With the proper charging and discharging of EVs with vehicle-to-grid (V2G), electrical system can get controllable storage/generations and at the same time, EV owners can earn profits. This paper presents a novel dispatch strategy to determine, when and at what rate EV battery should charge/discharge in order to maximize profits to EV owners and responding to power system's requirements while considering the effect of renewable DG power availability. This objective depends on many criteria like buying and selling price of energy, battery state of charge (SoC), Renewable DG power availability, and load leveling. To solve these, a new multi-criteria decision analysis method, Probabilistic Elimination and Choice Expressing Reality (p-ELECTRE), is developed. The proposed optimal dispatch strategy is applied to 100 and 200 EV fleets with random travel plan. Further, the effect of these fleets with optimal dispatch strategy is tested on IEEE 33 bus distribution system with added DGs. Furthermore, optimal power dispatch of DGs in EV-rich distribution system is obtained by bat optimization algorithm (BOA). The simulation results demonstrate the feasibility and benefits of the proposed technique.

关键词： distributed generations (DGs) electric vehicles (EVs) vehicle-to-grid (V2G) p-ELECTRE bat optimization algorithm optimal DG scheduling

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Placement and sizing of battery energy storage for primary frequency control in an isolated section of the Mexican power system

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ELECTRIC POWER SYSTEMS RESEARCH 2018年 160卷 142-150页

作者： Ramirez, Miguel Castellanos, Rafael Calderon, Guillermo Malik, Om INEEL Cuernavaca 62490 Morelos Mexico Univ Calgary Schulich Sch Engn Dept Elect & Comp Engn Calgary AB T2N 1N4 Canada

Increasing penetration levels of inverter-interfaced generation impose challenging frequency control problems to power system operation since frequency response capabilities are reduced as conventional generation is displaced by renewable energy sources. To face these challenges, alternatives such as battery Energy Storage Systems (BESSs) are necessary to provide advanced and enhanced frequency support. However, improper location and size of BESSs may greatly affect the performance and economic cost of their particular application. Based on this, an approach for the placement and sizing of BESS for primary frequency support in an isolated power system is presented in this study. BESS location and size are determined according to the most severe contingency for generation outage and different penetration levels of converter based renewable generation in the test system. Under these considerations, the transmission system bus with the larger frequency decline is identified for BESS placement. On the other hand, BESS sizing is formulated as a constrained optimization problem, with a defined cost function to be minimized. An iterative process based on the bat optimization algorithm (BOA) is used in this work to determine the selected parameters to be optimized. For comparison purposes, a Genetic algorithm (GA) approach is also included to deal with the formulated optimization problem. Simulation results show that system frequency response can be improved with the approach proposed in this study. Besides, the use of BOA based alternative is seen to perform relatively better than the GA approach in this case. (C) 2018 Elsevier B.V. All rights reserved.

关键词： battery energy storage Location and sizing of BESS bat optimization algorithm Primary frequency support Renewable energy sources

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Real-Time Machine Learning-based fault Detection, Classification, and locating in large scale solar Energy-Based Systems: Digital twin simulation

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SOLAR ENERGY 2023年第1期251卷 77-85页

作者： Cao, Hanhua Zhang, Dongming Yi, Shujuan Guangzhou Xinhua Univ Sch Informat & Intelligent Engn Guangzhou 510520 Peoples R China Heilongjiang Bayi Agr Univ Coll Engn Daqing 163000 Peoples R China

The current study considers numerous renewable energy resources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) in order to propose a reliable large-scale energy management framework that can be applied to islanded and grid-connected operations of renewable hybrid AC-DC microgrids (MGs). The framework uses a bat optimization algorithm (BOA) for minimizing the operating costs of the network and in addition introduces an intrusion detection system (IDS) according to the sequential hypothesis testing (SHT) method for detecting identity-enabled cyber-attacks (i.e classification of Sybil attacks, masquerading attacks) on the wireless-enabled advanced metering infrastructures (AMI). The suggested IDS uses the received signal strength (RSS) amount for distinguishing various signal resources and detecting cyberattacks. An IEEE 33-bus testing system has been used to construct a real-time hybrid MG in order to determine the reliability and efficiency of the suggested framework.

关键词： Energy management Machine learning bat optimization algorithm Microgrid Cyber security

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