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Secure video communication using firefly optimization and visual cryptography

ARTIFICIAL INTELLIGENCE REVIEW 2022年第4期55卷 2997-3017页

作者： Kumar, Manoj Aggarwal, Jyoti Rani, Anuj Stephan, Thompson Shankar, Achyut Mirjalili, Seyedali Univ Petr & Energy Studies UPES Sch Comp Sci Dehra Dun Uttarakhand India Graph Era Deemed Be Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India GL Bajaj Inst Technol & Management Dept Comp Sci Greater Noida India MS Ramaiah Univ Appl Sci Fac Engn & Technol Dept Comp Sci & Engn Bangalore Karnataka India Amity Univ Dept Comp Sci & Engn Noida India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

In recent years, we face an increasing interest in protecting multimedia data and copyrights due to the high exchange of information. Attackers are trying to get confidential information from various sources, which brings the importance of securing the data. Many researchers implemented techniques to hide secret information to maintain the integrity and privacy of data. In order to protect confidential data, histogram-based reversible data hiding with other cryptographic algorithms are widely used. Therefore, in the proposed work, a robust method for securing digital video is suggested. We implemented histogram bit shifting based reversible data hiding by embedding the encrypted watermark in featured video frames. Histogram bit shifting is used for hiding highly secured watermarks so that security for the watermark symbol is also being achieved. The novelty of the work is that only based on the quality threshold a few unique frames are selected, which holds the encrypted watermark symbol. The optimal value for this threshold is obtained using the Firefly algorithm. The proposed method is capable of hiding high-capacity data in the video signal. The experimental result shows the higher capacity and video quality compared to other reversible data hiding techniques. The recovered watermark provides better identity identification against various attacks. A high value of PSNR and a low value of BER and MSE is reported from the results.

关键词： Video watermarking RDH Visual cryptography Firefly algorithm Privacy preserving Optimization meta-heuristics algorithm

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An effective sizing and sensitivity analysis of a hybrid renewable energy system for household, multi-media and rural healthcare centres power supply: A case study of Kaele, Cameroon

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年第PartC期49卷 1321-1359页

作者： Kohole, Yemeli Wenceslas Fohagui, Fodoup Cyrille Vincelas Ngouleu, Clint Ameri Wankouo Tchuen, Ghislain Univ Dschang Dept Energet Environm & Thermal Engn IUT FV Bandjoun UR ISIE POB 134 Bandjoun Cameroon Univ Bamenda Coll Technol Dept Elect & Elect Engn POB 39 Bambili Cameroon Univ Dschang Fac Sci Dept Phys UR 2MSP POB 69 Dschang Cameroon

The vast majority of rural Africans lack access to clean, reliable, and sustainable electrical energy. This has deprived rural inhabitants of the opportunity to receive advanced healthcare delivery and modern education. In this study, an off-grid hybrid system composed of solar panels, wind turbines, battery banks and diesel-powered generators has been designed to fulfil the electrical loads requirements of a household, a multi-media and healthcare centres situated at Kaele, Cameroon. Initially, the wind and solar potential of Kaele were investigated and four meta-heuristics algorithms, namely, the colliding bodies' optimization, charged system search, teaching-learning-based optimization (TLBO) and the water evaporation optimization algorithms are applied to the hybrid system and the results are compared with regards to the system net present cost (NPC). Thereafter, three off-grid hybrid systems: PV/Wind/Battery/Diesel, PV/Battery/Diesel, and Wind/Battery/ Diesel were compared based on a techno-economic and environmental analysis. The results showed that, the mean yearly wind speed and solar radiation of Kaele are 6.7 m/s and 7.20 kWh/m2/day, respectively. Comparing the search power of the aforementioned algorithms on the sizing problem, it has been found that the TLBO algorithm produces better results than the other algorithms. The results also showed that, the PV/Wind/Battery/ Diesel configuration is the cost-effective option for supplying the household, multi-media and healthcare centres. The obtained optimal combination and the NPC of the PV/Wind/ Battery/Diesel system are as follows: For household, the most appropriate configuration combines 9 solar panels, 2 wind turbines, 33 battery banks, 1 diesel generator and the obtained NPC corresponds to 26111.2$;for the multi-media centre, 52 solar panels, 2 wind turbines, 96 battery banks and 1 diesel generator which resulted to a NPC of 55511.2$;and for the healthcare centre, the ideal configuration consists of 13 s

关键词： Healthcare centre Household Hybrid system meta-heuristics algorithm Optimal design PV/Wind/battery/diesel

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Optimal design of a parallel robotic gripper using enhanced multi-objective ant lion optimizer with a sensitivity analysis approach

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ASSEMBLY AUTOMATION 2020年第5期40卷 703-721页

作者： Mahanta, Golak Bihari BBVL, Deepak Biswal, Bibhuti B. Rout, Amruta Natl Inst Technol Rourkela Dept Ind Design Rourkela India

Purpose From the past few decades, parallel grippers are used successfully in the automation industries for performing various pick and place jobs due to their simple design, reliable nature and its economic feasibility. So, the purpose of this paperis to design a suitable gripper with appropriate design parameters for better performance in the robotic production systems. Design/methodology/approach In this paper, an enhanced multi-objective ant lion algorithm is introduced to find the optimal geometric and design variables of a parallel gripper. The considered robotic gripper systems are evaluated by considering three objective functions while satisfying eight constraint equations. The beta distribution function is introduced for generating the initial random number at the initialization phase of the proposed algorithm as a replacement of uniform distribution function. A local search algorithm, namely, achievement scalarizing function with multi-criteria decision-making technique and beta distribution are used to enhance the existing optimizer to evaluate the optimal gripper design problem. In this study, the newly proposed enhanced optimizer to obtain the optimum design condition of the design variables is called enhanced multi-objective ant lion optimizer. Findings This study aims to obtain optimal design parameters of the parallel gripper with the help of the developed algorithms. The acquired results are investigated with the past research paper conducted in that field for comparison. It is observed that the suggested method to get the best gripper arrangement and variables of the parallel gripper mechanism outperform its counterparts. The effects of the design variables are needed to be studied for a better design approach concerning the objective functions, which is achieved by sensitivity analysis. Practical implications The developed gripper is feasible to use in the assembly operation, as well as in other pick and place operations in different industries.

关键词： Automation Optimal gripper design Optimization Multi-objective optimization meta-heuristics algorithm

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meta-Heuristic Optimized Hybrid Wavelet Features for Arrhythmia Classification

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Intelligent Automation & Soft Computing 2023年第7期37卷 745-761页

作者： S.R.Deepa M.Subramoniam R.Swarnalatha S.Poornapushpakala S.Barani Sathyabama Institute of Science and Technology ChennaiTamil Nadu600119India School of EEE Sathyabama Institute of Science and TechnologyChennaiTamil Nadu600119India Department of EEE BITS PilaniDubai CampusDubai International Academic CityP.O Box No.345055DubaiUAE

The non-invasive evaluation of the heart through EectroCardioG-raphy(ECG)has played a key role in detecting heart *** analysis of ECG signals requires years of learning and experience to interpret and extract useful information from ***,a computerized system is needed to classify ECG signals with more accurate results *** heart rhythms are called arrhythmias and cause sudden cardiac *** this work,a Computerized Abnormal Heart Rhythms Detection(CAHRD)system is developed using ECG *** consists of four stages;preprocessing,feature extraction,feature optimization and *** first,Pan and Tompkins algorithm is employed to detect the envelope of Q,R and S waves in the preprocessing *** uses a recursive filter to eliminate muscle noise,T-wave interference and baseline *** the analysis of ECG signal in the spatial domain does not provide a complete description of the signal,the feature extraction involves using frequency contents obtained from multiple wavelet filters;bi-orthogonal,Symlet and Daubechies at different resolution levels in the feature extraction ***,Black Widow Optimization(BWO)is applied to optimize the hybrid wavelet features in the feature optimization ***,a kernel based Support Vector Machine(SVM)is employed to classify heartbeats into five *** SVM,Radial Basis Function(RBF),polynomial and linear kernels are used.A total of∼15000 ECG signals are obtained from the Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)arrhythmia database for performance evaluation of the proposed CAHRD *** show that the proposed CAHRD system proved to be a powerful tool for ECG *** correctly classifies five classes of heartbeats with 99.91%accuracy using an RBF kernel with 2nd level wavelet *** CAHRD system achieves an improvement of∼6%over random projections with the ensemble SVM approach and∼2%over morphological and ECG segment based features w

关键词： Arrhythmia classification abnormal heartbeats wavelets meta-heuristics algorithm neural network signal classification

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Modeling the supply chain network in the fast-moving consumer goods industry during COVID-19 pandemic

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OPERATIONAL RESEARCH 2023年第1期23卷 14页

作者： Asgharizadeh, Ezzatollah Daneshvar, Amir Homayounfar, Mahdi Salahi, Fariba Amini Khouzani, Mohsen Univ Tehran Dept Ind Management Tehran Iran Islamic Azad Univ Dept Informat Technol Management Elect Branch Tehran Iran Islamic Azad Univ Dept Ind Management Rasht Branch Rasht Iran Islamic Azad Univ Dept Ind Management Elect Branch Tehran Iran Islamic Azad Univ Dept Financial Engn Shahr Eqods Branch Tehran Iran

The outbreak of the COVID-19 pandemic in recent years has raised serious concerns about the distribution of fast-moving consumer goods products, given the freshness of their use. On the one hand, the distribution of fast-moving consumer goods with multiple vehicles has led to maintaining the freshness of items at the supply chain level, and on the other hand, it involves the high costs of using vehicles. Congestion of vehicles and drivers in the distribution of items has also increased the possibility of COVID-19 transmission. The importance of the above issue has led to the modeling of a multi-level supply chain problem in the FMCG industry by considering the freshness of items to reduce COVID-19 transmission. The most important issue considered in this article is to send fresh food in the shortest possible time to customers who cannot go to stores and wait in line to buy items in the conditions of Covid-19. Therefore, the designed model provides the possibility for customers to receive fresh food in addition to reducing costs and also reduce the possibility of contracting Covid-19. Designed supply chain network levels include suppliers of raw materials, manufacturers of consumer goods, distributors and end customers. In order to optimize the objectives of the problem, including minimizing the total costs of supply chain network design and maximizing the freshness of items, various strategic and tactical decisions such as locating potential facilities, routing vehicles, and optimally allocating the flow of goods should be made. Since the supply chain network model is considered to be NP-hard, meta-heuristic algorithms have been used to solve the problem by providing a modified priority-based encoding. The results show the high efficiency of the proposed solution method in a short time.

关键词： Fast-moving consumer goods Product freshness Shelf life COVID-19 pandemic Supply chain network design meta-heuristics algorithm Location-routing problem

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An enhanced human learning optimisation algorithm for effective data clustering

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2024年第2期15卷 127-142页

作者： Goyal, Jigyasa Kumar, Yugal Kumar, Pardeep Kaur, Arvinder Infosys Bangalore India Jaypee Univ Informat Technol Dept Comp Sci & Engn & Informat Technol Solan Himachal Prades India Chandigarh Engn Coll CGC Dept Informat Technol Landran Punjab India

Clustering arranges the data objects into clusters and similar data objects put into same cluster. There is not a single algorithm that can work effectively with all types of clustering problems. Other side, quality of clusters is also an important issue for clustering algorithms. This work deals with aforementioned issues and presents a new clustering algorithm-based human learning optimisation. Several modifications are incorporated into HLO algorithm for alleviating similar ability of individual learning and control parameter issues, called enhanced HLO (EHLO). The similar learning ability of individuals can be enhanced based on learner phase of TLBO algorithm. The control parameter issue of random learning is resolved through logistic chaotic map. The well-known clustering data sets are chosen for conducting the experiments and results are compared to eight state-of-the-art clustering algorithms based on several performance metrics. The results showed that EHLO obtains superior clustering results than other algorithms. The performance is also assessed through Friedman statistical test and it is followed by a post-hoc test. Results indicated that the proposed EHLO algorithm obtains an average rank of 1.29 compared to other algorithms, the p-value (3.71E-04) and the critical value (16.919). Hence, the proposed EHLO is validated through experimental and statistical results.

关键词： cluster analysis data clustering heuristics human learning optimisation meta-heuristics algorithm

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ANFIS-based improved GWO: Rapid prototyping of low-power solar energy system under fast-changing solar radiation conditions

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JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2024年第2期17卷

作者： Gokkus, Goksel Nevsehir Haci Bektas Veli Univ Elect & Elect Engn Dept TR-50300 Nevsehir Turkiye

The increasing need for energy, coupled with progress in technology, has emphasized the importance of renewable energy sources around the world. Solar energy systems harness the power of sunlight to generate electricity, offering a renewable and environmentally friendly alternative to conventional energy sources. Maximum Power Point Tracking (MPPT) algorithms are employed to achieve optimal efficiency from the PV systems. This paper introduces a new method for controlling Maximum Power Point (MPP) in battery charging systems using an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based improved Grey Wolf Optimization (ANFIS-GWO) algorithm. The study involves the application and evaluation of the IGWO algorithm under fastchanging solar radiation conditions (1000 w/m 2 , 600 w/m 2 , 800 w/m 2 ) and constant ambient temperature (25 degrees C). An analysis was performed to evaluate the behavior of the system in the laboratory environment using rapid prototyping. The proposed IGWO algorithm demonstrates improved accuracy compared to traditional methods in the current literature. The study aims to enhance the efficiency of MPPT control in photovoltaic (PV) systems, offering a potential solution for optimizing performance in different solar radiation conditions.

关键词： MPPT Grey wolf optimization ANFIS PV meta-heuristics algorithm

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Novel routing mechanism for IoT-based emergency rescue scenarios

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INTERNATIONAL JOURNAL OF MOBILE COMMUNICATIONS 2019年第4期17卷 465-482页

作者： Chaudhry, Rashmi Tapaswi, Shashikala Atal Bihari Vajpayee Indian Inst Informat Technol Gwalior India

Routing in emergency rescue scenarios where nodes around the site are mobile and send the information about the service needs to the concerned service providers through internet connectivity is an important issue. Focus is done by researchers in the respective fields for vehicular ad hoc networks (VANETs) and wireless sensor networks (WSNs) but the scenarios having mobile nodes around the site and having IoT environment have not been addressed. As this type of case includes time constraint-based services, thus a new mechanism predicting mobility of nodes at gateway and multicasting of the required information accordingly, is proposed in this paper. meta-heuristic-based algorithm, grasshopper optimisation algorithm (GOA) is modified to fit for the prediction and then applied for taking decision related to multicast routing named as grasshopper-based routing and mobility (GRAM) algorithm. This prediction helps the network to send updates to the emergency site with minimal cost and energy consumption. Analysis of results shows the improved performance of the proposed mechanism in terms of both speed and accuracy.

关键词： emergency management routing meta-heuristics algorithm mobility prediction

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An examination of the hybrid meta-heuristic machine learning algorithms for early diagnosis of type II diabetes using big data feature selection

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Healthcare Analytics 2023年 4卷 100227-100227页

作者： Navazi, Fatemeh Yuan, Yufei Archer, Norm Information Systems Department DeGroote School of Business McMaster University Hamilton ON Canada

People are increasingly getting type II diabetes mellitus (T2DM) due to unhealthy food styles, decreased outdoor activities caused by the COVID-19 pandemic, and unawareness of type II diabetes risk factors. This disease is hidden in the early stages and causes many comorbidities like fatty liver, heart disease, and peripheral artery disease. This study presents several hybrid algorithms to diagnose T2DM in its early stages without requiring expensive and time-consuming medical tests. We first apply feature selection using the Particle Swarm Optimization (PSO) algorithm to reduce the required computations. meta-heuristics are used in developed hierarchical algorithms to optimize the hyperparameters of machine learning algorithms for classification. A comparative analysis of the algorithms with performance metrics shows Genetic algorithm-Support Vector Machine (GA-SVM) has the largest area under the Receiver Operating Characteristic (ROC) curve (0.934) and better performance in most metrics (Accuracy of 0.934 and F1-Measure of 0.945) and reasonable metaheuristic computational time. Therefore, the GA-SVM algorithm is recommended for clinical decision support systems. This algorithm diagnoses T2DM at early stages by responding to several questions with about 93% accuracy, which can help patients to survive future complications through lifestyle intervention therapy. © 2023 The Author(s)

关键词： Big data Chronic disease Feature selection Machine learning meta-heuristics algorithm Type II diabetes diagnosis

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