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Pairing-Free Identity-Based Digital Signature algorithm for Broadcast Authentication Based on Modified ECC Using Battle Royal optimization algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期123卷 2341-2365页

作者： Kumar, Vivek Ray, Sangram Natl Inst Technol Sikkim Dept Comp Sci & Engn Sikkim 737139 India

In wireless sensor networks (WSN), broadcast authentication is an important security service that provides secure communication. Several mobile users are allowed by this broadcast authentication service. There are some concerns in the sensor network, which depend on security based on maintaining consumer untracking and privacy for data transmission. To overcome the concern of security issues, an Identity (ID)-based cryptography method is introduced. This paper presented a Pairing-Free Identity-based Digital Signature (PF-IBDS) algorithm based on Modified Elliptic Curve Cryptography (MECC) by using Battle Royal optimization algorithm. The main aim of this paper is to secure the data transmission for message authentication. The proposed protocol is to enhance the speed of authentication, reduce the signature size and speed up the signature verification. Moreover, this paper analyses the security by BAN (Burrows-Abadi-Needham) logic and comparing with the existing protocols. It is verified that the developed protocol is protected and well-organized for peer-to-peer communications. Therefore, the proposed method offers secure key management, fast authentication, and also minimizes the computation overhead. The proposed authentication process is simulated in the Java platform. The performance of this protocol has compared with other existing approaches in terms of key verification time, key generation time and computational time.

关键词： Key generation Authentication WSN optimization algorithm Security Elliptic curve cryptography Identity-based signature Pairing-free

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Improved Reptile Search optimization algorithm Using Chaotic Map and Simulated Annealing for Feature Selection in Medical Field

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IEEE ACCESS 2022年 10卷 51428-51446页

作者： Elgamal, Zenab Sabri, Aznul Qalid Md Tubishat, Mohammad Tbaishat, Dina Makhadmeh, Sharif Naser Alomari, Osama Ahmad Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Ajman U Arab Emirates Univ Sharjah MLALP Res Grp Sharjah U Arab Emirates

The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles' encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA;the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm optimization (PSO), Genetic algorithm (GA), Grasshopper optimization algorithm (GOA) and Slime Mould optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets.

关键词： Reptile search algorithm (RSA) feature selection (FS) optimization algorithm chaos theory simulated annealing (SA)

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Non-invasive method of melanoma detection on the skin surface through extraction of image features using modified CAT optimization algorithm

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CURRENT SCIENCE 2023年第5期124卷 562-569页

作者： Prabhakaran, N. Aalim Muhammed Salegh Coll Engn Dept Elect & Commun IAF Chennai 600055 India

In this study, melanoma was detected at an early stage using modified CAT optimization algorithm (MCOA) based on non-convex boundary edge extraction, pixel size, shape and intensity variations on the skin. MCOA can detect skin cancer at an early stage by extracting the non-convex border of the affected region prevent cancer spread. Thus melanoma is curable when detected at an early stage. MCOA extracts image features and obtains non-convex boundaries of melanoma in the skin image. The non-convex boundary region leads to visua-lization of discriminative features of melanoma based on the region of interest and scaling. The proposed MCOA delineates the affected region through non-convex border extraction and edge detection. An accuracy of 85% was obtained in the detection of melanoma using MCOA, when compared to traditional algorithms.

关键词： Contour refinement edge detection mela-noma non-convex boundary optimization algorithm

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The defect of the Grey Wolf optimization algorithm and its verification method

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KNOWLEDGE-BASED SYSTEMS 2019年第May 1期171卷 37-43页

作者： Niu, Peifeng Niu, Songpeng Liu, Nan Chang, Lingfang Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Hebei Peoples R China

Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in terms of model structure. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars. In the course of its research, this paper find that GWO is flawed. It has good performance for the optimization problem whose optimal solution is 0, however, for other problems, its advantage is not as obvious as before or even worse. Then it is further found that when GWO solves the same optimization function, the farther the function's optimal solution is from 0, the worse its performance, and this flaw also appears in other optimization algorithms. Through the study of this defect, the analysis is carried out, and the reason is determined. Finally, although there is no way to make GWO normal, this paper provides a verification method to avoid the same problem, and hopes to help the development of the optimization algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Meta-heuristic Gray Wolf optimization algorithm optimization algorithm Defect New verification method

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Go-Caterpillar Mutation and Its optimization algorithm for Synthesis of Large-Scale Sparse Planar Arrays

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第3期71卷 2194-2207页

作者： Zhao, Heng Ban, Yong-Ling Liu, Yanhui Hu, Jun Nie, Zaiping Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China

Optimizing the layout of sparse planar arrays constrained by minimum element spacing to reduce the peak sidelobe level (PSLL) is a difficult and challenging task in engineering applications. Here, a new sparse array design method is proposed under the constraints of aperture size, the number of array elements, and minimum spacing between elements. The approach is based on a new element mutation method which is proposed for mutating the position of any element within the aperture without changing the position of other elements. Because a mutating element can be thought of as being placed inside the board like a black/white stone in go or crawling somewhere nearby like a caterpillar, we call it go-caterpillar-mutation (GCM). Based on GCM, a stochastic optimization algorithm (GCM-OA) is proposed to optimize the layout of sparse planar arrays. Several examples demonstrate the robustness and rapidity of GCM-OA in reducing PSLL by adjusting the array element positions under various constraints.

关键词： Finite element analysis Apertures Planar arrays Layout Stochastic processes Encoding Antenna radiation patterns Antenna array go-caterpillar-mutation (GCM) optimization algorithm position perturbation model (PPM) sparse planar array

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RL based hyper-parameters optimization algorithm (ROA) for convolutional neural network

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第10期14卷 13349-13359页

作者： Talaat, Fatma M. Gamel, Samah A. Kafrelsheikh Univ Fac Artificial Intelligence Kafrelsheikh Egypt Mansoura Univ Fac Engn Dept Comp Engn & Syst Mansoura Egypt

Many real-world applications necessitate optimization in dynamic situations, where the difficulty is to locate and follow the optima of a time-dependent objective function. To solve dynamic optimization problems (DOPs), many evolutionary techniques have been created. However, more efficient solutions are still required. Recently, a new intriguing trend in dealing with optimization in dynamic environments has developed, with new reinforcement learning (RL) algorithms predicted to breathe fresh life into the DOPs community. In this paper, a new Q-learning RL-based optimization algorithm (ROA) for CNN hyperparameter optimization is proposed. Two datasets were used to test the proposed RL model (MNIST dataset, and CIFAR-10 dataset). Due to the use of RL for hyperparameter optimization, very competitive results and good performance were produced. From the experimental results, it is observed that the CNN optimized by ROA has higher accuracy than CNN without optimization. When using the MNIST dataset, it is shown that the accuracy of the CNN optimized by ROA when learning 5 epoch is 98.97%, which is greater than the 97.62% of the CNN without optimization. When using the CIFAR-10 dataset, it is shown that the accuracy of the CNN optimized by ROA when learning 10 epoch is 73.40 percent, which is greater than 71.73% of the CNN without optimization.

关键词： optimization algorithm Deep learning Deep convolutional neural networks Reinforcement learning

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Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm

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APPLIED ENERGY 2020年 277卷 115503-115503页

作者： Heydari, Azim Nezhad, Meysam Majidi Pirshayan, Elmira Garcia, Davide Astiaso Keynia, Farshid De Santoli, Livio Sapienza Univ Dept Astronaut Elect & Energet Engn DIAEE Via Eudossiana 18 I-00184 Rome Italy Sapienza Univ Rome Dept Planning Design & Technol Architecture Rome Italy Grad Univ Adv Technol Dept Energy Management & Optimizat Inst Sci & High Technol & Environm Sci Kerman Iran

Electricity price forecasting is a key aspect for market participants to maximize their economic efficiency in deregulated markets. Nevertheless, due to its non-linearity and non-stationarity, the trend of the price is usually complicated to predict. On the other hand, the accuracy of short-term electricity price and load forecasting is fundamental for an efficient management of electric systems. An accurate prediction can benefit future plans and economic operations of the power systems' operators. In this paper, a new and accurate combined model has been proposed for short-term load forecasting and short-term price forecasting in deregulated power markets. It includes variational mode decomposition, mix data modeling, feature selection, generalized regression neural network and gravitational search algorithm. A mixed data model for the price and load forecast has been considered and integrated with the original signal series of price and load and their decomposition. Throughout this model, the candidate input variables are chosen by a distinct hybrid feature selection. Two reliable electricity markets (Pennsylvania-New Jersey-Maryland and Spanish electricity markets) have been used to test the proposed forecasting model and the obtained results have been compared with different valid benchmark prediction models. Lastly, the real load data of Favignana Island's power grid have been considered to test the proposed model. The obtained results pinpointed that the proposed model's precision and stability is higher than in other benchmark forecasting models.

关键词： Electricity price forecasting Load forecasting Generalized Regression Neural Network Feature selection optimization algorithm Power grid management

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optimization of Self-Learning in Computer Engineering Courses: An Intelligent Software System Supported by Artificial Neural Network and Vortex optimization algorithm

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2017年第1期25卷 142-156页

作者： Kose, Utku Arslan, Ahmet Usak Univ Comp Sci Applicat & Res Ctr Usak Turkey Konya Food & Agr Univ Dept Comp Engn Konya Turkey

Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed self-learning activity process supports both learners and teachers to achieve educational objectives rapidly. Because of this, there has always been a remarkable trend on developing alternative self-learning approaches. In this context, this study is based on two essential objectives. Firstly, it aims to introduce an intelligent software system, which optimizes and improves computer engineering students' self-learning processes. Secondly, it aims to improve computer engineering students' self-learning during the courses. As general, the software system introduced here evaluates students' intelligence levels according to the Theory of Multiple Intelligences and supports their learning via accurately chosen materials provided over the software interface. The evaluation mechanism of the system is based on a hybrid Artificial Intelligence approach formed by an Artificial Neural Network, and an optimization algorithm called as Vortex optimization algorithm (VOA). The system is usable for especially technical courses taught at computer engineering departments of universities and makes it easier to teach abstract subjects. For having idea about success of the system, it has been tested with students and positive results on optimizing and improving self-learning have been obtained. Additionally, also a technical evaluation has been done previously, in order to see if the VOA is a good choice to be used in the system. It can be said that the whole obtained results encourage the authors to continue to future works. (C) 2017 Wiley Periodicals, Inc.

关键词： self-learning theory of multiple intelligences artificial neural networks vortex optimization algorithm computer engineering

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A Group Teaching optimization algorithm for Priority-Based Resource Allocation in Wireless Networks

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期123卷 2449-2472页

作者： Sreethar, S. Nandhagopal, N. Karuppusamy, S. Anbu Dharmalingam, M. Excel Engn Coll Dept Comp Sci & Engn Namakkal Tamil Nadu India Excel Engn Coll Dept ECE Namakkal India Kongunadu Coll Engn & Technol Dept ECE Thottiam 621215 Tamil Nadu India

In wireless networks, resource allocation (RA) is considered as a very important and it is used to enhance the wireless and communication networks performances. D2D (Device-to-device) communication based RA is expressed as a MINLP (mixed integer nonlinear programming) issue. Existing strategies based on RA cannot offer integrative consideration to spectrum efficiency, QoS (Quality of Service) for networks with mixed traffic. When multiple users with various priorities utilize the similar network resource, then it is necessary to utilize the resource on priority basis. In this work, priority based RA is introduced using the GTOA (group teaching optimization algorithm) by combining spectrum resources from unlicensed and licensed spectrum bands to the D2D users in the mixed traffic scenarios. The GTOA optimization framework maximizes all the D2D pair's utility functions by reducing the whole energy utilization. The network contains the two types of users such as QoS services and BE (best effort) services. Along with several traffics, the function of utility is described for users. The priorities are assigned to the QoS services by the developed GTOA based RA mechanism for performing the resource allotment. As related to other current approaches, the GTOA procedure devours lessor complexity. For the real-time traffic, the developed GTOA can spontaneously assurance the QoS constraint and also owing to the utility function the developed GTOA system create the balance among fairness and throughput for BE traffic users. As a result, by executing RA, the resources and a user can be deliberated. The priority based RA is performed using MATLAB. Simulation outcomes show that the developed GTOA is most appropriate for the priority based RA to the D2D users in the mixed traffic scenarios.

关键词： Priority optimization algorithm Mixed traffic D2D communication Resource allocation

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An orthogonal array based optimization algorithm for computer-aided measurement of worm surface

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2006年第5-6期30卷 434-443页

作者： Mohan, L. V. Shunmugam, M. S. Indian Inst Technol Dept Mech Engn Mfg Engn Sect Madras 600036 Tamil Nadu India

Being versatile and fast, a co-ordinate measuring machine is used for the measurement of worm. A best-fit surface is obtained from the measured points by a surface fitting method, which minimizes the root mean square of normal deviations. For this problem in discrete space, an iterative optimization algorithm based on an orthogonal array is developed. On minimizing the objective function, the deviations of worm parameters from the specified values are obtained. The algorithm is validated using input data points generated from a straight-sided in axial section worm (ZA worm) with known errors. The proposed algorithm requires fewer objective function evaluations and the result is highly repeatable as there are no random operations involved.

关键词： worm measurement optimization algorithm orthogonal array

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