Dolphin Echolocation Algorithm Research Articles

DEA-RNN: A HYBRID DEEP LEARNING APPROACH FOR CYBERBULLYING DETECTION IN TWITTER SOCIAL MEDIA PLATFORM

Cyberbullying has emerged as a pervasive issue within social media platforms, posing significant risks to users' well-being. To address this growing concern and enhance platform safety, this paper proposes a novel hybrid deep learning model, termed DEA-RNN, designed specifically for detecting cyberbullying on Twitter. The DEA-RNN model integrates Elman-type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) to fine-tune RNN parameters and expedite training. Through comprehensive evaluation using a dataset comprising 10,000 tweets, we compared DEA-RNN's performance with state-of-theart algorithms such as Bi-directional Long Short Term Memory (Bi-LSTM), SVM, Multinomial Naive Bayes (MNB), and Random Forests (RF). Our experimental findings demonstrate the superiority of DEA-RNN across all scenarios, showcasing its effectiveness in cyberbullying detection on the Twitter platform. Particularly noteworthy is DEA-RNN's exceptional performance in scenario 3, achieving an average accuracy of 90.45%, precision of 89.52%, recall of 88.98%, F1-score of 89.25%, and specificity of 90.94%.

Optimal design of semi-rigid connection steel frame with infilled shear walls using dolphin echolocation algorithm

In this paper, the dolphin echolocation algorithm (DEA) was applied for optimization. Two different two-dimensional semi-rigid connection steel frames with infilled shear walls (SFIWs) were optimized to obtain the minimum weight with constraints on the element stresses and storey drift ratio according to the American Institute of Steel Construction (AISC) Load and Resistance Factor Design requirements. With the proposed method, the specific details of the infilled shear walls are not given during the design process, but are characterized by the lateral stiffness. The designer can decide which types of infilled shear walls are used according to the result obtained from the optimization. The infilled shear walls are modeled as braces with equivalent lateral stiffnesses, while the semi-rigid connections are used to obtain the practical behaviors of the SFIWs. The results demonstrate that the proposed method is suitable for optimizing semi-rigid connection steel frames with infilled shear walls.

Critical Failure Mode Determination of Steel Moment Frames by Plastic Analysis Optimization Principles

Determining the failure or failure mode of structures has long been a challenge for civil engineers. Traditional methods for analyzing structures are costly and complex. Plastic analysis, which involves combining pre-defined mechanisms, offers a less complex approach. However, as the number of potential mechanism combinations, or the search space, increases with the growing complexity of structural members, the effectiveness of this method diminishes. To address this issue, optimizers have been applied in the field of structural engineering to efficiently solve problems with large search spaces. Population-based meta-heuristic algorithms are widely used for their reduced dependency on input parameters. This research focuses on implementing the plastic theory of steel frames using MATLAB software, employing virtual work concepts and pre-defined mechanism combinations. A novel binary dolphin echolocation algorithm is proposed based on the principles of the primary algorithm. This algorithm is then utilized to optimize the plastic analysis method and determine the failure load factor and critical failure mode for sample frames. Additionally, the grey wolf optimizer and whale optimization algorithm are applied to optimize the problem, and the performance of all three algorithms is compared. The results demonstrate that the proposed algorithm yields accurate results with a minor margin of error compared to the other two algorithms.

A Survey on Cyberbullying Classification and Detection

Social media platforms have seen an increase in the prevalence of cyberbullying. Protecting social media platforms against cyberbullying is essential as social media is extensively used among people of all ages. Events of cyberbullying have been rising, especially among young individuals who spend most of their time switching between various social media sites. This study gives an overview of the existing research on the categorization and detection of cyberbullying using several methods from the Deep learning and Machine learning field like Convolutional Neural Network, Recurrent Neural Network, Long Short -Term Memory, Gated Recurrent Unit, Bi-GRU-Attention-CapsNet, Support Vector Machine, Random Forest, Naive Bayes, and k-Nearest Neighbor, along with the study that examines the effects of various feature extraction techniques like Term frequency and Inverse Document Frequency, Information Gain, Dolphin Echolocation Algorithm, and Improved Dolphin Echolocation Algorithm.

Optimal Design of Semirigid Connection Steel Frame with Steel Plate Shear Walls Using Dolphin Echolocation Algorithm

Steel frame with steel plate shear walls (SPSWs) is used to resist lateral loads caused by wind and earthquakes in high-rise buildings. In this load-resisting system, the cost and performance are more efficient than in the moment frame system. Behaviors of beam-to-column connections are assumed to be pinned or fixed to simplify the calculation in the past few decades. However, studies have stated that such a simulation fails to reveal the response of beam-to-column connections. In this paper, a newly developed metaheuristic optimization algorithm—the dolphin echolocation algorithm (DE)—based on foraging prey using echolocation in dolphins is applied as the present study optimizer. Two different two-dimensional semirigid connection steel frames with SPSWs are optimized to obtain the minimum cost of semirigid connection steel frame with steel plate shear walls with constraints to element stresses and story drift ratio according to the American Institute of Steel Construction (AISC) Load and Resistance Factor Design (LFRD). SPSW is modeled as a brace with equivalent lateral stiffness, while the P−△ effects are considered in the steel frame. Semirigid connections are used to reveal the actual responses of beam-to-column connections. The results demonstrate the proposed method’s effectiveness for optimizing semirigid connection steel frames with SPSWs and the interaction between semirigid connections and the SPSWs.

Virtual Machine Migration-Based Intrusion Detection System in Cloud Environment Using Deep Recurrent Neural Network

Cloud system attracts users with the desired features, and in the meanwhile, cloud system may experience various security issues. An effective intrusion detection system is offered by the proposed Sail Fish Dolphin Optimization-based Deep Recurrent Neural Network (SFDO-based Deep RNN), which is utilized to identify anomalies in the cloud architecture. The developed SFDO is formed by integrating Sail Fish Optimizer (SFO) and Dolphin Echolocation (DE) algorithm. Virtual Machine (VM) migration and cloud data management are accomplished using ChicWhale algorithm. Some of the attribute features are collected from the cloud model such that these features are grouped using Fuzzy C-Means (FCM) clustering. The feature fusion process is carried out by a Deep RNN classifier that has been trained using the specified SFDO technique in order to achieve the intrusion detection mechanism. The approach with the lowest error value is thought to be the best approach for intruder detection, according to the fitness function. Using the BoT-IoT dataset, the proposed method’s accuracy, detection rate (DR), and false-positive rate (FPR) were assessed. The results showed that it outperformed the previous methods with values of 0.9614, 0.9648, and 0.0429, respectively.

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network. The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for fine-tuning the Elman RNN’s parameters and reducing training time. We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). The experimental results show that DEA-RNN was found to be superior in all the scenarios. It outperformed the considered existing approaches in detecting CB on Twitter platform. DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.

Fractional squirrel–dolphin echolocation with deep belief network for network‐controlled vertical handoff in disparate and heterogeneous wireless network

SummaryThe innovative characteristics of different heterogeneous wireless networks provide better service. The disparate wireless access networks consist of wireless metropolitan access network (WMAN) and cellular networks. Various techniques are devised for dynamic network selection, but these techniques concentrated on attaining improved performance. To address this issue, a novel method is devised to improve the energy efficiency of disparate heterogeneous wireless networks. Here, the proposed fractional squirrel–dolphin echolocation (FrSqDE) algorithm is devised by integrating squirrel search optimization (SSA) and fractional dolphin echolocation (fractional DE) algorithm. In addition, the fitness function is considered with certain factors that include delay, bit error rate (BER), jitter, packet loss, energy consumption, probability of hot spot, average bit rate (ABR), and received signal strength (RSS). The proposed FrSqDE optimization algorithm is used for training deep belief networks (DBNs) to select optimal weights. Here, the network selection factors are given as input to the proposed FrSqDE‐based DBN, wherein optimal decision is made to handle vertical handoff. The proposed FrSqDE‐based DBN offered improved performance with a minimal call drop probability of 0.050, minimal delay of 0.013 s, minimal energy consumption of 0.393 J, and maximal throughput of 117.972 kbps.

A network controlled vertical handoff mechanism for heterogeneous wireless network using optimized support vector neural network

PurposeVertical handoff mechanism (VHO) becomes very popular because of the improvements in the mobility models. These developments are less to certain circumstances and thus do not provide support in generic mobility, but the vertical handover management providing in the heterogeneous wireless networks (HWNs) is crucial and challenging. Hence, this paper introduces the vertical handoff management approach based on an effective network selection scheme.Design/methodology/approachThis paper aims to improve the working principle of previous methods and make VHO more efficient and reliable for the HWN.Initially, the handover triggering techniques is modelled for identifying an appropriate place to initiate handover based on the computed coverage area of cellular base station or wireless local area network (WLAN) access point. Then, inappropriate networks are eliminated for determining the better network to perform handover. Accordingly, a network selection approach is introduced on the basis ofthe Fractional-dolphin echolocation-based support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train Support vector neural network (SVNN) for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.FindingsThe performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput. The proposed Fractional-DE method achieves the minimal delay of 0.0100 sec, the minimal energy consumption of 0.348, maximal staytime of 4.373 sec, and the maximal throughput of 109.20 kbps.Originality/valueIn this paper, a network selection approach is introduced on the basis of the Fractional-Dolphin Echolocation-based Support vector neural network (Fractional-DE-based SVNN). The Fractional-DE is designed by integrating Fractional calculus (FC) in Dolphin echolocation (DE), and thereby, modifying the update rule of the DE algorithm based on the location of the solutions in past iterations. The proposed Fractional-DE algorithm is used to train SVNN for selecting the best weights. Several parameters, like Bit error rate (BER), End to end delay (EED), jitter, packet loss, and energy consumption are considered for choosing the best network.The performance of the proposed VHO mechanism based on Fractional-DE is evaluated based on delay, energy consumption, staytime, and throughput, in which the proposed method offers the best performance.

Seismic Design Optimization of Steel Frames with Steel Shear Wall System Using Modified Dolphin Algorithm

The first step in any successful design of a structure is selecting the exact and accurate designing method. Designing a structure most economically (i.e. materials such as steel and concrete) by satisfying seismic and gravity criteria based on codes of practice is a complicated procedure that requires not only technical engineering knowledge but also years of experience. Thus, this paper presents an efficient and reliable approach to obtain the optimum design in steel frames with shear walls as the main seismic bearing component. The main objective is to use a modified dolphin echolocation algorithm with a multi-variability ability approach in achieving the optimized results. This approach represents the best positioning option for steel shear walls in elevations, spans, and dimensions of the elements. According to modifications applied to the algorithm, the computational time decreases, and the results are highly accurate. The designed structures with the minimum amount of steel satisfy all the requirements in the seismic design and steel structures design code, and the results of this work are entirely efficient. This efficiency is illustrated by three numerical examples. The results illustrated the superiority of the metaheuristic approach over the standard procedure. Besides, considering the weight of shear wall for the first time led to the 10% reduction of the weight of the structure.

Dolphin echolocation algorithm for small-signal stability analysis of DFIG-based wind power system

Dolphin echolocation algorithm for small-signal stability analysis of DFIG-based wind power system

Dolphin echolocation algorithm for small-signal stability analysis of DFIG-based wind power system

Dolphin echolocation algorithm for small-signal stability analysis of DFIG-based wind power system

An efficient imperialist competitive algorithm with likelihood assimilation for topology, shape and sizing optimization of truss structures

This article presents an efficient hybrid meta-heuristic algorithm for topology, layout and sizing optimization of truss structures. A new assimilation scheme is implemented in the imperialist competitive algorithm (ICA) in order to improve computational efficiency, the likelihood of occurrence and the neighborhood patterns are used, and the assimilation step of the ICA is enhanced. In this method, the probabilities are assigned to each alternative by the imperialist and its neighbors in the search space; then, the colonies construct new solutions (moving to the relevant imperialist) based on the likelihood of occurrence. Neighborhood patterns are proposed to gather information from the neighboring countries in order to extract features based on the local power variation. In this study, the extended abilities of the proposed algorithm are inspired from the dolphin echolocation (DE) algorithm and the cellular automata (CA) method, which the new algorithm is denoted as CA-ICEA. The optimization results obtained by ICA, DE and CA-ICEA methods are compared. Remarkably, the proposed algorithm outperforms its competitors in terms of optimum weights, their mean and standard deviation.

RETRACTED: A hybrid technique used in grid integration of photovoltaic system for maximum power point tracking with multilevel inverter

A new hybrid technique for grid integration of solar photovoltaic system using modified incremental conductance maximum power point tracking algorithm with multi-output converter and multilevel inverter is proposed in the paper. The proposed hybrid technique is the joint execution of improved dolphin echolocation algorithm and gradient boosting decision trees and this way the proposed technique is named as IDE-GBDT technique. The novelty of the proposed work is the dolphin echolocation algorithm, which is integrated by the crossover and the mutation function so it is named as IDE. In the proposed technique, the multi-output converter is the combination of boost converter and switched capacitor function to generate different self-balanced output voltages. The utilization of multilevel inverter in the proposed system provides better quality of output voltage and current waveform thereby reducing the size of passive filters. Also, eliminates the requirement of bulky transformers for grid integration. Multicarrier unipolar phase disposition pulse width modulation technique is employed for triggering the switches of the multilevel inverter. The maximum power point tracking algorithm uses the estimated active power output of the generator as its input and generates command speed so that maximum power is transferred to the dc link. This control system also incorporates a loss minimization approach to minimize the losses in the generator and hence to improve the efficiency of the photovoltaic system. Finally, the performance of the proposed maximum power point tracking control of wind and photovoltaic power generation schemes is executed in Matrix Laboratory/Simulink working platform and the execution is assessed with the existing techniques. The proposed technique is compared with the existing techniques and the observed total harmonic distortion of the proposed technique in all the cases is 0.67%, 0.51%, 0.58%, 0.63%, 0.92%, and 1.03% and the total harmonic distortion is found to be very less compared with existing techniques.

S-DolLion-MSVNN: A Hybrid Model for Developing the Super-Resolution Image From the Multispectral Satellite Image

Abstract Super-resolution offers a new image with high resolution from the low-resolution (LR) image that is highly employed for the numerous remote sensing applications. Most of the existing techniques for formation of the super-resolution image exhibit the loss of quality and deviation from the original multi-spectral LR image. Thus, this paper aims at proposing an efficient super-resolution method using the hybrid model. The hybrid model is developed using the support vector regression model and multi-support vector neural network (MSVNN), and the weights of the MSVNN is tuned optimally using the proposed algorithm. The proposed DolLion algorithm is the integration of the dolphin echolocation algorithm and lion optimization algorithm that exhibits better convergence and offers a global optimal solution. The experimentation is performed using the datasets taken from the multi-spectral scene images. The optimal and effective formation of the super-resolution image using the proposed hybrid model outperforms the existing methods, and the analysis using the second-derivative-like measure of enhancement (SDME) ensures that the proposed method is better and yields a maximum SDME of 67.6755 dB.

A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

In this paper, we integrate the autonomous search paradigm on a swarm intelligence algorithm in order to incorporate the auto-adjust capability on parameter values during the run. We propose an independent procedure that begins to work when it detects a stagnation in a local optimum, and it can be applied to any population-based algorithms. For that, we employ the autonomous search technique which allows solvers to automatically re-configure its solving parameters for enhancing the process when poor performances are detected. This feature is dramatically crucial when swarm intelligence methods are developed and tested. Finding the best parameter values that generate the best results is known as an optimization problem itself. For that, we evaluate the behavior of the population size to autonomously be adapted and controlled during the solving time according to the requirements of the problem. The proposal is testing on the dolphin echolocation algorithm which is a recent swarm intelligence algorithm based on the dolphin feature to navigate underwater and identify prey. As an optimization problem to solve, we test a machine-part cell formation problem which is a widely used technique for improving production flexibility, efficiency, and cost reduction in the manufacturing industry decomposing a manufacturing plant in a set of clusters called cells. The goal is to design a cell layout in such a way that the need for moving parts from one cell to another is minimized. Using statistical non-parametric tests, we demonstrate that the proposed approach efficiently solves 160 well-known cell manufacturing instances outperforming the classic optimization algorithm as well as other approaches reported in the literature, while keeping excellent robustness levels.

Fuzzy Inspired Deep Belief Network for the Traffic Flow Prediction in Intelligent Transportation System Using Flow Strength Indicators.

Intelligent transportation system (ITS) is an advance leading edge technology that aims to deliver innovative services to different modes of transport and traffic management. Traffic flow prediction (TFP) is one of the key macroscopic parameters of traffic that supports traffic management in ITS. Growth of the real-time data in transportation from various modern equipments, technology, and other resources has led to generate big data, posing a huge concern to deal with. Recently, deep learning (DL) techniques have demonstrated the capability to extract comprehensive features efficiently, using multiple hidden layers, from such huge raw, unstructured, and nonlinear data. Nonlinearity in traffic data is the major cause of inaccuracy in TFP. In this article, we propose a flow strength indicator-based Chronological Dolphin Echolocation-Fuzzy, a bioinspired optimization method with fuzzy logic for incremental learning of deep belief network. Technical indicators provide flow strength features as an input to the model. Hidden layers of DL architecture consequently learn more features and propagate it as an input to next layer for supervised learning. The degree of membership to the features is identified by the membership functions, followed by weight optimization using Dolphin Echolocation algorithm to fit the model for the nonlinear data. Experiments performed on two different data sets, namely Traffic-major roads and performance measurement system-San Francisco (PEMS-SF), show good results for the proposed deep architecture. The analysis of the proposed method using log mean square error and log root mean square deviation acquires a minimum value of 2.4141 and 0.61 for the Traffic-major roads database taken for the time step duration of 1 year and a minimum value of 1.6691 and 0.5208 for PEMS-SF data set for the time step interval of 5 minutes, respectively. These positive results demonstrate key importance of our traffic flow model for the transportation system.

E‐TDGO: An encrypted trust‐based dolphin glowworm optimization for secure routing in mobile ad hoc network

SummaryMobile ad hoc networks (MANETs) are independent networks, where mobile nodes communicate with other nodes through wireless links by multihop transmission. Security is still an issue to be fixed in MANETs. Hence, a routing protocol named encrypted trust‐based dolphin glowworm optimization (DGO) (E‐TDGO) is designed using Advanced Encryption Standard‐128 (AES‐128) and trust‐based optimization model for secure routing in MANET. The proposed E‐TDGO protocol includes three phases, namely, k‐path discovery, optimal path selection, and communication. At first, k paths are discovered based on the distance and the trust level of the nodes. From the k paths discovered, the optimal path is selected using a novel algorithm, DGO, which is developed by combining glowworm swarm optimization (GSO) algorithm and dolphin echolocation algorithm (DEA). Once the optimal path is selected, communication begins in the network such that E‐TDGO protocol ensures security. The routing messages are encrypted using AES‐128 with shared code and key to offer security. The experimental results show that the proposed E‐TDGO could attain throughput of 0.11, delay of 0.01 second, packet drop of 0.44, and detection rate of 0.99, at the maximum number of rounds considered in the network of 75 nodes with attack consideration.

Hybrid optimisation for priority‐based scheduling in multi‐user STBC‐MIMO‐OFDM

Resource allocation in wireless communication systems is of great concern in order to guarantee the efficient utilisation of scary resources. In space–time block codes-based multiple-input multiple-output with orthogonal frequency division multiplexing (STBC-MIMO-OFDM) system, scheduling the appropriate user to the available antenna is the major challenge. To overcome the challenge, this study proposes a priority-based scheduling mechanism using the hybrid optimisation, Dolphin-Rider Optimisation (DRO), which is a combination of Dolphin Echolocation Algorithm and Rider Optimisation Algorithm. The prioritisation follows the power and quality of service constraints of the users in such a way that the energy efficiency is enabled in the system. The performance of the system is analysed using the Rayleigh and Rician channels with the transmission media as, text, audio, and image. Moreover, the comparative analysis of the proposed DRO is enabled using three modulation schemes, Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying, and quadrature amplitude modulation. The proposed user scheduling mechanism with the BPSK modulation is found to be effective with a minimal bit error rate and maximal throughput of 3.78×10−7 and 0.875.

Dolphin Echolocation Based Generation of Application Definite Noc Custom Topology

Invention of new electronic devices in this technology driven-world has been setting a new scale for faster reach in terms of utilization and communication. The imperfection in the System on Chip (SoC) lead to an innovation of Network on Chip (NoC) that elucidates the communication defects and thereby set a new path for the researchers to enhance the network connectivity. Nature is an inspiration for most of the technologies invented by the researchers. In this paper, we have dealt the existing physical flows and future difficulties with Reliable Reconfigurable Real-Time Operating System (R3TOS) as a software interface with NoC with the proposed customized topology of Transmission Rate based Topology Design using Dolphin Echolocation (TRTD-DE) algorithm. Echolocation is biological scanning system that used by dolphins for movement and chasing the prey. The above ability along with Data flow pipelining (DFP) constructs the customized topology that process the parallel flow of data in order with the availability of the input data. The topology effectively increases the performance due to the clustering phenomena. The algorithm greatly reduces the latency period which is the significant of the topology and increases the throughput. The objectives of the research in the reduction of transmission rate and energy utilization are tested with different multimedia benchmark applications. The transmission rate reduced to average of 41.39% while average of energy consumption reduced to 31.9%.