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Mobile Edge Computing (MEC), which is the main technology behind 5th Generation (5G) networks, is a nascent paradigm that meets the demands of IoT (Internet of Things) and localized computing. From an end-user point of view, it can be regarded as a refinement of cloud computing and Fog computing. The transfer of computational tasks to the closet MEC servers leads to energy efficiency and low latency. It is important to apply the most suitable policies for scheduling, especially when configuring different modules of an IoT application in MEC. In this paper, three algorithms, namely the heuristic Bald Eagle Search Optimisation [BESO] algorithm, Particle Swarm Optimization algorithm [PSO], and Genetic Algorithm [GA], are presented to carry out heuristic offloading of computational tasks with a view to improving the latency and performance of MEC. However, the most effective algorithm must be adopted to conduct these tasks. As a result, this paper attempts to find an algorithm that is most appropriate for MEC. To demonstrate this. the three algorithms were tested in the Long-Term Evolution [LTE] based Orthogonal frequency-division multiplexing [OFDM] network during a period when the edge nodes had no adequate resources. The performance and efficiency of the three algorithms, BESO, PSO and GA, were determined and compared. After generating the results, the comparative results were evaluated. In terms of offloading the computational tasks, the BESO algorithm was discovered to perform better, with greater energy efficiency and lower latency, than the other two algorithms.

Compared with traditional observation satellites, agile earth observation satellites are capable of prolonging observation time windows (OTWs) for targets, which significantly alleviates observation conflicts, thereby facilitating imaging data collection. However, it also leads to more uncertainties in determining the start time to image targets within these longer OTWs for an agile satellite network (ASN) to collect imaging data. Furthermore, these collected data is offloaded only within short transmission time windows between data collectors and data sinks, thus resulting in a transmission scheduling problem. Toward this end, this paper investigates joint observation and transmission scheduling in ASNs, aiming at accommodating more imaging data to be collected and offloaded successfully. Specifically, we formulate the studied problem as integer linear programming (ILP) to maximize the weighted sum of scheduled imaging tasks. Then, we explore the hidden structure of this ILP and transform it into a special framework, which can be solved efficiently through semidefinite relaxation (SDR). To reduce computation complexity, we further propose a fast yet efficient algorithm by combining the advantages of the devised SDR method and a genetic algorithm with special population initialization. Finally, simulation results demonstrate that the proposed algorithm can significantly increase the weighted sum of scheduled tasks.

Modern transportation is associated with considerable challenges related to safety, mobility, the environment and space limitations. Vehicular networks are widely considered to be a promising approach for improving satisfaction and convenience in transportation. However, with the exploding popularity among vehicle users and the growing diverse demands of different services, ensuring the efficient use of resources and meeting the emerging needs remain challenging. In this paper, we focus on resource allocation in vehicular cloud computing (VCC) and fill the gaps in the previous research by optimizing resource allocation from both the provider's and users' perspectives. We model this problem as a multi-objective optimization with constraints that aims to maximize the acceptance rate and minimize the provider's cloud cost. To solve such an NP-hard problem, we improve the nondominated sorting genetic algorithm II (NSGA-II) by modifying the initial population according to the matching factor, dynamic crossover probability and mutation probability to promote excellent individuals and increase population diversity. The simulation results show that our proposed method achieves enhanced performance compared to the previous methods.

To accurately predict the regional spread of coronavirus disease 2019 (COVID-19) infection, this study proposes a novel hybrid model, which combines a long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arising from confounding variables underlying the spread of the COVID-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries at the time of the study. The results show that the proposed model closely replicates the test data, such that not only it provides accurate predictions but it also replicates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model while accounting for data limitation. The parameters of the hybrid models are optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict the short-term to medium-term daily spreading of the COVID-19 infection, it is capable of being used for policy assessment, planning, and decision making.

This paper combines the tabu search process with a genetic algorithm by employing a new partial opposed-based as the population initialization technique to minimize makespan. Flow shop is a prominent variety in scheduling with various applications in numerous disciplines, including production. Its difficulty makes many researchers develop algorithms to solve it since it is categorized as an NP-hard problem. Some approaches have been developed to resolve production scheduling, especially evolutionary algorithms. The genetic algorithm becomes the most used algorithm in evolutionary to address production schedule because of its capability to produce good, fast, and efficient results to explore complex solution space (global search). However, it provides ineffective results when doing exploitation (local search) that is easily stuck in the optimum local area. Contrarily, tabu search has superiority in local search to help genetic algorithms not be trapped in a local optimum. By combining these two algorithms with the initialization scheme, a new algorithm that balances searches in resulting higher quality solutions is obtained. The proposed algorithm was tested using 120 problem instances to carry out the algorithm performance. The empirical results state that the developed algorithm can improve 115 solutions out of 120 instances than the six other hybrid algorithms.

In this paper, we first propose a multi-objective task-scheduling optimization problem that minimizes both the makespans and total costs in a fog-cloud environment. Then, we suggest an optimization model based on a Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II) to deal with the discrete multi-objective task-scheduling problem and to automatically allocate tasks that should be executed either on fog or cloud nodes. The NSGA-II algorithm is adapted to discretize crossover and mutation evolutionary operators, rather than using continuous operators that require high computational resources and not able to allocate proper computing nodes. In our model, the communications between the fog and cloud tiers are formulated as a multi-objective function to optimize the execution of tasks. The proposed model allocates computing resources that would effectively run on either the fog or cloud nodes. Moreover, it efficiently organizes the distribution of workloads through various computing resources at the fog. Several experiments are conducted to determine the performance of the proposed model compared with a continuous NSGA-II (CNSGA-II) algorithm and four peer mechanisms. The outcomes demonstrate that the model is capable of achieving dynamic task scheduling with minimizing the total execution times (i.e. makespans) and costs in fog-cloud environments

Due to the lack of improper access control policies and decentralized access controllers, security and privacy-aware peer-to-peer (P2P) energy trading among electric vehicles (EVs) and the smart grid is challenging. Most of the solutions reported in the literature for P2P energy trading are based upon centralized controllers having various security flaws resulting in their limited applicabilities in real-world scenarios. To handle these issues, in this paper, we propose a P2P energy trading scheduling scheme called as P2P Energy Trading Scheduling (PETS) using blockchain technology. PETS is based on real-time energy consumption monitoring for balancing the energy gap between service providers (SPs), i.e., smart grids and service consumers, i.e., EVs. In PETS, the Stackelberg game theory-based 1-leader multiple-followers scheme is proposed to depict the interactions between EVs and the SP. The selection of the leader among all SPs is made using a second-price reverse auction. As per the announced energy price by the leader, EVs manage energy consumption by minimizing their energy bills. In PETS, on the leader's side, we propose the Genetic algorithm to maximize its profit. In contrast, on the followers' side, i.e., EVs, we use the Stackelberg Equilibrium to minimize their energy bills. Simulation results demonstrate that the proposed PETS scheme outperforms the existing state-of-the-art schemes using various performance evaluation metrics. Specifically, it reduces the peak-to-average ratio (PAR) by 12.5% of EVs' energy load in comparison to the existing state-of-the-art scheme.

In-situ tool condition monitoring (In-situ TCM) is vital for metal removal manufacturing which realizes on-machine diagnosis in a real-time manner. The limitation of in-situ TCM based on traditional deep learning lies in several aspects: the requirement of sufficient labeled data of health conditions, the empirically manual designed architecture and the labor-intensive tuning of hyper-parameters. Network-based deep transfer learning (NDTL) partially solves the problem of limited labeled data. However, the time-consuming architecture design and the tuning of hyper-parameters also pose a great impact on the schedule of real in-situ TCM projects. In this paper, a new NDTL method is proposed, which is automatically designed by the genetic algorithm (NDTL-GA). A degradation monitoring experiment is conducted employing edge computing devices under multiple working conditions, where the texture of the machined surface is collected during the whole life cycle of milling cutters. The experimental results suggest that the proposed method possesses competitive performance evaluated by both the robustness metrics (e.g., the area under the curve of precision-recall (AUC-PR)) and the efficiency metrics (e.g., multiplyaccumulates (MACC)), where the trial-and-errors are reduced significantly by the automatic architecture design and the selection of hyper-parameters.

In recent history, Convolutional Neural Networks have attained breakthroughs in addressing many intractable problems in the domain of image processing. But its performance builds upon its chosen hyper parameters and it is a tedious job to manually fine tune these hyper parameters. Hence, in this research, an Automated Hyper-parameter Optimized Convolution Neural Net is proposed which is further applied to uncover the class of skin cancer. The method has utilized a Grey Wolf Optimization algorithm for optimizing the hyper parameters of CNN, by adopting a proper encoding scheme. The effectiveness of the model is verified by comparing it with the performance of Particle Swarm Optimization and Genetic algorithm based hyper-parameter optimized CNN applied on the International Skin Imaging Collaboration skin lesion multi class data set. Simulation results infer that the proposed model is able to produce a testing accuracy up to 98.33% which is around 4% and 1% more compared to PSO and GA based models respectively. Similarly with the proposed model, the testing loss realized is around 0.17% which is 39.2% and 15% less compared to PSO and GA based models respectively. The experimental results clearly demonstrate that the proposed model performs competitively compared to other reported models.

Deep learning has been proved to be one of the most effective method in feature encoding for different intelligent applications such as video based human action recognition. However, its non-convex optimization mechanism leads large memory consumption, which hinders its deployment on the smart embedded systems with limited computation resources. To overcome this challenge, we propose a novel deep model compression technique for smart embedded systems, which realizes both the memory size reduction and inference complexity decrease within a small drop of accuracy. First, we propose an improved naive Bayes inference based channel parameter pruning to obtain a sparse model with higher accuracy. Then, to improve the inference efficiency, the improved Tucker decomposition method is proposed, where an improved genetic algorithm is used to optimize the Tucker ranks. Finally, to elevate effectiveness of our proposed method, extensive experiments are conducted. The experimental results show that our method can achieve the state-of-the-art performance compared with existing methods in terms of accuracy, parameter compression and floating point operations reduction.

Driver Assistance Systems (DAS) have been progressively incorporated into commercial vehicles in recent years. All these systems are paving the way for the forthcoming autonomous vehicle which will become a reality in the near future. Existing systems are based on numerous electronic systems with advanced skills, high performances, and high degrees of adaptability and intelligence. As is to be expected, these cutting-edge features require, in most cases, the use of powerful computing platforms. However, the deployment of such platforms is not an easy task, since they have to be integrated in the vehicle where there exist important restrictions regarding size, power consumption and cost. In this sense, every smart proposal aimed at reducing the complexity of these systems without degrading performance, is always a valuable contribution in the field. In this work, we propose a methodology to reduce the dimensionality of a driver distraction recognition system. The methodology is based on a multi-objective genetic algorithm that looks for the minimum set of useful features collected during the driving task and also for the simplest recognition system. The recognition algorithm is an Extreme Learning Machine (ELM) whose simplicity and fast learning procedure make it especially suitable to be used by a Genetic Algorithm which needs to evaluate thousands of candidate solutions. The proposed methodology has been tested with a real-world database collected from different drivers performing an itinerary with an instrumented car. The results obtained validate the proposal as a method to reduce the complexity of a driver distraction recognition system.

Autonomous learning algorithms operate in an online fashion in dealing with data stream mining, where minimum computational complexity is a desirable feature. For such applications, parsimonious learning machines (PALMs) are suitable candidates due to their structural simplicity. However, these parsimonious algorithms depend upon predefined thresholds to adjust their structures in terms of adding or deleting rules. Besides, another adjustable parameter of PALM is the fuzziness in membership grades. The best set of such hyper parameters is determined by experts' knowledge or by optimization techniques such as greedy algorithms. To mitigate such experts' dependency or usage of computationally expensive greedy algorithms, in this work, a meta heuristic-based optimization technique, called the multimethod-based optimization technique (MOT), is utilized to develop an advanced PALM. The performance has been compared with some popular optimization techniques, namely, the greedy search, local search, genetic algorithm (GA), and particle swarm optimization (PSO). The proposed parsimonious learning algorithm with MOT outperforms the others in most cases. It validates the multioperator-based optimization technique's advantages over the single operator-based variants in selecting the best feasible hyperparameters for the autonomous learning algorithm by maintaining a compact architecture.

Wireless Sensor Network (WSN) plays a vital role in dealing with the challenging tasks of information management in Intelligent Transportation Systems (ITS). Current research has shown that the ever growing number of vehicles on the roads is making congestion worse, and many safety concerns are being addressed comprehensively by the WSN-based ITS. However, the energy consumed by sensor nodes, their operational period, and `security compromise' are ever growing concerns. Cluster-based routing strategies have potentially contributed in reducing the energy expenditure of sensor nodes, besides the selection of energy-efficient and secure Cluster Head (CH) is still seeking an optimized approach for acquiring the proliferated performance of WSN. To address these concerns, we propose an Intelligent Clustering approach for ITS (ICITS) which selects CHs based on a hybrid optimization method called GABAT that integrates the strengths of Genetic Algorithm (GA) and BAT Algorithm (BA). The proposed framework (ICITS) is targeted primarily to road transport in military areas due to their stringent requirements in terms of security and reliability while collecting the data from the deployed sensor nodes. The simulation results obtained with ICITS demonstrate that it performs well for various performance metrics that include stability period, network survival period and `number of packets sent', which are improved by 54.7%, 19.6%, and 40.5%, respectively as compared to recently proposed Cluster-based Intelligent Routing Protocol (CIRP).

This paper proposes a new reconfigurable intelligent surface based three-dimensional beam tracking method to solve the beam tracking problems for the unmanned aerial vehicle with obstacles in communication channels. The proposed beam tracking method can not only regulate the reconfigurable intelligent surfaces to achieve the beam tracking of the obstructed communications, but also optimize the transmission efficiency of the communication. Firstly, a reconfigurable intelligent surface is proposed, which can correlate the transmission signals by adjusting the phase-shift matrix. Meanwhile, a new communication channel is constructed according to the reconfigurable intelligent surface, which consists of two parts. The first one is the channel between the unmanned aerial vehicle and the reconfigurable intelligent surface, the other is the channel between the reconfigurable intelligent surface and the ground base station. Note that the transmission performance of the communications can be optimized by adjusting the phase shift of each uniform linear array on the reconfigurable intelligent surface. Then, a new beam tracking method for the unmanned aerial vehicle with obstructed communications is proposed on the basis of the reconfigurable intelligent surface. By proposing the mixed genetic algorithm, the estimation accuracy of the azimuth and elevation angles is improved to enhance the performance of the beam tracking. Finally, the simulations are provided to verify the effectiveness of the proposed three-dimensional beam tracking method with reconfigurable intelligent surface.

In order to overcome the demerits of traditional city air terminals (CAT), a brand-new service mode based on urban mobile stations (UMS) is introduced in this article for providing the luggage check-in service in urban area. Different from CAT, for each service time period, the UMSs are located based on the real-time passenger distributions. The objectives are to improve the service quality and enhance the competitiveness of air transportation. First, the system components and operation mode of UMS are described. Second, a mathematical model is set up for locating UMSs and assigning passengers to service stations. Three aspects, including the average path length from passengers to UMSs, the maximum acceptable path length for passengers, and the maximum service capacity of a station are considered. Third, to meet the strict time requirement of the UMS system, an effective evolutionary algorithm for optimizing UMS locations in real time is further developed. In the method, the ripple-spreading algorithm is applied for solving the many-to-many path optimization problem and a two-stage adaptive genetic algorithm is utilised for efficiently locating UMSs. Finally, a test case is set up based on the city centre of Tianjin, China, and passenger source locations are obtained by surveying passengers at Tianjin Airport. The reported method is then applied to dynamically locate UMSs in Tianjin urban area and it is compared to some other existing methods to show its effectiveness and efficiency. The service quality of UMS is compared to CAT in both cases with realistic data and randomly generated data to show its advantages.

An Autonomous Vehicle (AV) is believed to be the next generation transport that can enhance safety and efficiency for smart mobility. In an AV-based public transportation system, the full autonomy of AVs enables high-efficiency transport services and the potential ride-sharing feature of AV system enhances the utilization. The system manages a fleet of AVs, determines their assignments to transport requests, sends instructions concerning the optimized plans, and recommends the parking locations for the unoccupied AVs. The parking location of an empty AV is crucial in the sense that rebalancing AVs to areas with high potential service demand can curtail the unnecessary waiting time for passengers. As AVs are generally electric, proper parking locations can also facilitate vehicle-to-grid (V2G) support. In this paper, we propose a joint rebalancing and V2G coordination strategy for AV-based public transportation system. We formulate the problem as an integer linear program and propose a heuristic based on Genetic Algorithm and Model Predictive Control to solve the problem in low time complexity. Extensive experiments are performed with the real taxi service data from New York City. The formulated integer linear program is solved dynamically where each problem instance contains 3 to 5 AVs and 3 to 8 requests in 30s time interval. Compared with the transport system without rebalancing, the results show that the coordination strategy is efficient and effective in reducing unnecessary waiting time for passengers while satisfying V2G support. Compared to computational time with the standard solver, the proposed heuristic dramatically reduces the computational time.

Feature selection is a basic task for pattern recognition and classification. It enhances the performance of the classification algorithms with the help of removing the redundant features. Thanks to eliminating irrelevant features, the computational time is decreased. Thus, intensive works have been carried out in this area. This paper proposes a new standard error-based artificial bee colony (SEABC) algorithm for the feature selection problem, which is developed by integrating standard error-based new solution search mechanisms into the original artificial bee colony algorithm. The SEABC algorithm is used for feature selection. Shannon entropy function is used to serve as the objective function of the SEABC algorithm. Thirteen datasets are used from UCI machine learning datasets. Features are selected according to Shannon conditional entropy values and then a threshold process is implemented to find their best relevant subset. Support Vector Machines (SVMs) and k-Nearest Neighbor (KNN) are used as the optimal classifiers. The proposed SEABC algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), ABC, improved ABC (I-ABC), Gbest-guided ABC (GABC), and PS-ABC algorithms. In general, it is observed that the SEABC algorithm achieves better classification results than other well-known algorithms.

A modular actuator construction consisting of smaller articulating units in series was designed to construct soft pneumatic actuators. These units are constructed to have a preferential bending direction using Mold Star 15 and Smooth-Sil 950 silicone. By changing the orientation of each unit, a different deformed actuator shape can be achieved. A design tool was developed where a genetic algorithm was coupled with a nonlinear finite element solver. This design tool optimizes the design using the genetic information available in the initial population over multiple generations and presents a candidate design that best resembles a desired profile specified as the objective function. A two-dimensional reduced order model was developed that reduces the time for each function evaluation from ≈20 min for a three-dimensional (3D) numerical analysis to ≈45 s. The design tool was tasked to solve design targets ranging from sine and cosine functions of various amplitudes to final actuator tip positions. In each case, the inflated actuator resembled the desired profile. Selected physical actuators were cast and tested. 3D scanning was used to capture the inflated shape and compared it to the numerical solution. A quantitative comparison showed a maximum average deviation of2.5% of the uninflated actuator length between the physical and numerical models. The proposed design tool proved successful in designing shape matching actuators with close agreement to physical models.

Mutation testing, a fundamental software testing technique, which is a typical way to evaluate the adequacy of test data. In mutation testing, a set of mutants are generated by seeding the different classes of faults into a program under test. Test data shall be generated in the way that as many mutants can be killed as possible. Thanks to numerous tools to implement mutation testing for different languages, a huge amount of mutants are normally generated even for small-sized programs. However, a large number of mutants not only leads to a high cost of mutation testing, but also make the corresponding test data generation a non-trivial task. In this paper, we make use of intelligent technologies to improve the effectiveness and efficiency of mutation testing from two perspectives. A machine learning technique, namely fuzzy clustering, is applied to categorize mutants into different clusters. Then, a multi-population genetic algorithm via individual sharing is employed to generate test data for killing the mutants in different clusters in parallel when the problem of test data generation as an optimization one. A comprehensive framework, termed as FUZGENMUT, is thus developed to implement the proposed techniques. The experiments based on nine programs of various sizes show that fuzzy clustering can help to reduce the cost of mutation testing effectively, and that the multi-population genetic algorithm improves the efficiency of test data generation while delivering the high mutant-killing capability. The results clearly indicate the huge potential of using intelligent technologies to enhance the efficacy and thus the practicality of mutation testing.

In this paper, an adaptive genetic algorithm is used to conduct an in-depth study and analysis of English text background elimination, and a corresponding model is designed. The curve results after the initial character editorialization are curved and transformed, and the adaptive genetic algorithm is used for the transformation to solve the influence of multiple inflection points of curve images on feature extraction. Then, using the minimum deviation method, the error values of the input characters and the sample set in the spatial coordinate system are calculated, and the deviation values of the angle and the straight line are used to match the characters with the smallest deviation value to match the highest degree. A genetic algorithm is introduced to iterate the feature sets of angles and line segments, and the optimal features are finally derived in the process of cross evolution of generations to improve the recognition accuracy. And the character library is used as input items for average grouping for experiments, and the obtained feature sets are put into the position matrix and compared with the samples in the database one by one. It is found that the improved stroke-structure feature extraction algorithm based on a genetic algorithm can improve the recognition accuracy and better accomplish the recognition task with better results compared to others. Finally, by analyzing the limitations and characteristics of traditional particle swarm optimization algorithm and differential evolution algorithm, and giving full play to the advantages and applicability of different algorithms, a new differential evolution particle swarm algorithm with better performance and more stable performance is proposed. The algorithm is based on the PSO algorithm, and when the population update of the PSO algorithm is stagnant and the search space is limited, the crossover and mutation operations of the DE algorithm are used to perturb the population, increase the diversity of the population, and improve the global optimization ability of the algorithm. The algorithm is tested on a common dataset for text mining to verify the effectiveness and feasibility of the algorithm.

The edge-cloud collaboration network is promising to support complex requirements with temporal constraints, where a requirement can be achieved through the composition of computation-demanding and delay-sensitive services. In this setting, most services should be optimally configured at the network edge, in order to decreasing service response latency and reducing network resource consumption. To address this challenge, this paper proposes an optimal service configuration mechanism, where temporal constraints between services are mined from event logs through our temporal interval discovery mechanism. Service configuration is formulated as a constrained multi-objective optimization problem, which is solved by our improved non-dominated sorting genetic algorithm II. Extensive experiments are conducted, and evaluation results demonstrate that our approach can find the close-to-optimal service configuration in comparison with the state-of-the-art techniques in terms of delay sensitivity and energy efficiency, especially when edge nodes can co-host a relatively large number of services.

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncertain nature of financial time-series data. In contrast, online learning mode evolving-structured ML models could be promising for financial time-series forecasting. For real-time deployment of such models, low memory demand is a must. Besides, the model's explainability plays a crucial role in forecasting financial time-series. Considering all the requirements, a rule-based autonomous neuro-fuzzy learning algorithm called the parsimonious learning machine (PALM) is proposed here to forecast time-varying stock indices. To provide efficient automation of the proposed algorithm by maintaining the model explainability in terms of limited number linguistic IF-THEN rules, two popular multiobjective evolutionary algorithms (MEAs), such as a real-coded genetic algorithm (GA) and a self-adaptive differential evolution (DE) algorithm are utilized here. In addition, fuzzy type-2 variants of PALMs' are considered here due to better uncertainty handling capacity than their type-1 counterparts. To evaluate the proposed algorithm's performance, the closing stock price of fifteen (15) different stock market indices are predicted here. From the results, it is observed that the MEA-based PALMs are performing better than the state-of-the-art benchmark online ML models and providing a rule-based explainable model to the end-user.

Coarse-grained reconfigurable architectures (CGRAs) provide high energy efficiency with word-level programmability rather than bit-level ones such as FPGAs. The coarser reconfigurability brings about higher energy efficiency and reduces the complexity of compiler tasks compared to the FPGAs. However, application mapping process for CGRAs is still time-consuming. When the compiler tries to map a large and complicated application data-flow-graph(DFG) onto the reconfigurable fabric, it tends to result in inefficient resource use or to fail in mapping. In case of failure, the compiler must divide it into several sub-DFGs and goes back to the same flow. In this work, we propose a novel partitioning method based on a genetic algorithm to eliminate the unmappable DFGs and improve the mapping quality. In order not to generate unmappable sub-DFGs, we also propose an estimation model which predicts the mappability and resource requirements using a DGCNN (Deep Graph Convolutional Neural Network). The genetic algorithm with this model can seek the most resource-efficient mapping without the back-end mapping process. Our model can predict the mappability with more than 98% accuracy and resource usage with a negligible error for two studied CGRAs. Besides, the proposed partitioning method demonstrates 53-75% of memory saving, 1.28-1.39x higher throughput, and better mapping quality over three comparative approaches.

This paper investigates the resilient control problem of networked switched systems against denial-of-service (DoS) attack. The resilient control strategies include the security game of the network layer and the optimal control for the physical layer. Firstly, an N-coalition noncooperative game is utilized to characterise the network security game between an intrusion detection system (IDS) and the attacker, where their strategy sets are obtained from the Nash equilibrium. Then, by the dynamic programming principles, the optimization strategy is developed to obtain the optimal controller gains for the subsystems. Meanwhile, for ensuring the optimality of the switched system, a genetic algorithm is adopted to search for the optimal switching signal. Finally, the proposed methods are applied to a networked continuous stirred tank reactors (CSTR) system which demonstrates its effectiveness.

Product design constitutes a critical process for a firm to stay competitive. Whilst the biologically inspired Clonal Selection Algorithms (CSA) have been applied to efficiently solve several combinatorial optimization problems, they have not yet been tested for optimal product lines. By adopting a previous comparative analysis with real and simulated conjoint data, we adapt and compare in this context 23 CSA variants. Our comparison demonstrates the efficiency of specific cloning, selection and somatic hypermutation operators against other optimization algorithms, such as Simulated Annealing and Genetic Algorithm. To further investigate the robustness of each method to combinatorial size, we extend the previous paradigm to larger product lines and different optimization objectives. The consequent performance variation elucidates how each operator shifts the search focus of CSAs. Collectively, our study demonstrates the importance of a fine balance between global and local search in such combinatorial problems, and the ability of CSAs to achieve it.

A dynamic self-assembly is a promising approach for inducing the collective behavior of agents to perform coordinated tasks at small scales. However, efficient pattern formation and navigation in environments with complex conditions remain a challenge. In this article, we propose a strategy for micromanipulation using dynamically self-assembled magnetic droplets with needle guidance. An iron needle was controlled by a three-degree-of-freedom (3-DoF) manipulator and magnetized by precessing magnetic fields. The process of self-assembly was optimized based on real-time vision feedback and a genetic algorithm. Affected by the locally induced field gradient near the needle, reconfigurable assembled magnetic droplets were formed beneath the air-liquid interface with high time efficiency, and the geometric center of the pattern was determined. Following the magnetized needle, assembled patterns were navigated along preplanned paths and exhibited reversible pattern expansion and shrinkage. Moreover, cargo can be trapped and caged by exploiting the induced fluid flow around the assembled droplets. To perform cargo transportation tasks in a multiple-obstacle environment, an optimal path planner with obstacle-avoidance capability was designed based on the particle swarm optimization (PSO) algorithm. Experiments demonstrated effective pattern formation, navigation, cargo trapping, and obstacle-avoidance transportation. The proposed method opens new prospects of using a dynamically self-assembled pattern as an untethered end-effector for micromanipulation.

This paper presents an automated design and optimization framework for electric transportation power systems (ETPS), enabled by machine learning (ML). The use of physical models, simulations, and optimization methods can greatly aid the engineering design process. However, when considering the optimal co-design of multiple inter-dependent subsystems that span multiple physical domains, such model-based simulations can be computationally expensive, and traditional metaheuristic optimization methods can be unreliable. Bayesian optimization (BO), a ML framework, paves one feasible pathway to realize an efficient design process practically. However, current state-of-the-art BO algorithms are non-compatible or perform poorly when applied to system-level ETPS design with multiple objectives and constraints. This paper proposes a novel BO algorithm referred to as Max-value Entropy Search for Multi-objective Optimization with Constraints (MESMOC) to solve multi-objective optimization (MOO) problems with black-box constraints that can only be evaluated through design simulations. After full presentation of the algorithm, MESMOC is applied to a realistic ETPS design case using a heavy-duty electric vertical-takeoff-landing (eVTOL) urban aerial vehicle (UAV) power system. Two MOO experimental trials show a drastic reduction in the number of design simulations to discover a high-quality Pareto front. In Trial 1, MESMOC uncovered the entire Pareto front while only requiring to explore ~4% of the design space. With expanded design parameters and a larger design space in Trial 2, a near-complete but high-quality Pareto front was uncovered. Both trials compared MESMOC to the popular genetic algorithm NSGA-II and another BO algorithm PESMOC, showing superior performance.

The primary goal of any financial investor is to maximize returns and minimize risks.Investment specialists and portfolio managers have to manually analyze assets, determine potential returns and risks, and adhere to client preferences to create optimal portfolios. It is a complex and gruelling process and demands significant skill and relevant experience for success. Like all other sectors in finance, machine learning models have seen tremendous success in the portfolio selection problem as well. In this paper, a thorough review of several machine learning portfolio optimization techniques such as Clustering-based, SVM based, Genetic Algorithm based, and more has been presented. This paper, in its entirety, condenses and interprets the numerous approaches and the merits and limitations corresponding to their implementation. The conclusions presented by this review can be utilized to identify the advantages of these papers. This will help future researchers in their study of the domain and ensure availability of essential data for further analysis in a systematic and comprehensive manner. With the help of this study, researchers will be well placed to identify areas that present scope for improvement and come up with novel or possibly hybrid techniques to achieve near perfection in Portfolio Selection

RNA structure prediction with pseudoknots is an NP-complete problem, in which an optimal RNA structure with minimum energy is to be computed. In past decades, several methods have been developed to predict RNA structure with pseudoknots. Among them, metaheuristic approaches have proven to be beneficial for predicting long RNA structure in a very short time. In this paper, we have used two metaheuristic algorithms; Genetic Algorithm (GA) and Simulated Annealing (SA) for predicting RNA secondary structure with pseudoknots. We have also applied a combination of these two algorithms as GA-SA where GA is used for a global search and SA is used for a local search, and conversely SA-GA, where SA is used for a global search and GA is used for a local search. Four different energy models have been applied to calculate the energy of RNA structure. Five datasets, constructed from the RNA STRAND and Pseudobase++ database, have been used in the algorithms. The performances of the algorithms have been compared with several existing metaheuristic algorithms. Here we have obtained that the combination of GA and SA (GA-SA) gives better results than GA, SA and SA-GA algorithms and all other four state-of-art algorithms on all datasets.

Since last few years, microarray technology has got tremendous application in many bio-medical researches. However, in order to efficiently recognize and apply this technology into the bio-medical areas is still very difficult and expensive. There are many metaheuristic approaches has been developed with different biological interpretation. Despite the existence of several approaches, there is always a requirement of development of more robust and efficient approach. In this work a new metaheuristic approach is proposed implementing binary shuffled frog leaping algorithm (BSFLA) for gene selection. To obtain an optimal gene subset, 20 different combination of gene subset is extracted from the original dataset. Out of which the optimal gene subset is identified implementing KNN classifier. Superiority of these gene set is shown using few other classifiers such as ANN and SVM. The model performance is also compared with few other metaheuristic approaches such as particle swarm optimization, differential evolution and genetic algorithm.

With the popularity of mobile devices, intelligent applications, e.g., face recognition, intelligent voice assistant, and gesture recognition, have been widely used in our daily lives. However, due to the lack of computing capacities, it is difficult for mobile devices to support complex Deep Neural Network (DNN) inference. To alleviate the pressure on these devices, traditional methods usually upload part of the DNN model to a cloud server and perform a DNN query after uploading an entire DNN model. To achieve real-time DNN query, we consider the collaboration between local, edge and cloud, and perform DNN query when uploading DNN partitions. In this paper, we propose an Efficient offloading scheme for DNN Inference Acceleration (EosDNN) in a local-edge-cloud collaborative environment, where the DNN inference acceleration is mainly embodied in the optimization of migration delay and realization of real-time DNN query. EosDNN comprehensively considers the migration plan and uploading plan, where for the former, a Particle Swarm Optimization with Genetic Algorithm (PSO-GA) is applied to obtain the distribution of DNN layers under the server with the lowest migration delay, and for the latter, a Layer Merge Uploading Algorithm (LMU) is proposed to obtain DNN partitions and their upload order with efficient DNN query performance. Experimental results demonstrate that EosDNN can be applied to large-scale DNN model migration, which can achieve an ideal migration delay and obtain a more fine-grained DNN partition uploading plan, thereby optimizing DNN query performance.

Deep Neural Networks (DNNs) have become an essential and important supporting technology for smart Internet-of-Things (IoT) systems. Due to the high computational costs of large-scale DNNs, it might be infeasible to directly deploy them in energy-constrained IoT devices. Through offloading computation-intensive tasks to the cloud or edges, the computation offloading technology offers a feasible solution to execute DNNs. However, energy-efficient offloading for DNN based smart IoT systems with deadline constraints in the cloud-edge environments is still an open challenge. To address this challenge, we first design a new system energy consumption model, which takes into account the runtime, switching, and computing energy consumption of all participating servers (from both the cloud and edge) and IoT devices. Next, a novel energy-efficient offloading strategy based on a Self-adaptive Particle Swarm Optimization algorithm using the Genetic Algorithm operators (SPSO-GA) is proposed. This new strategy can efficiently make offloading decisions for DNN layers with layer partition operations, which can lessen the encoding dimension and improve the execution time of SPSO-GA. Simulation results demonstrate that the proposed strategy can significantly reduce energy consumption compared to other classic methods.

Motivation A phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g. hybridization, reassortment, recombination) evolution. The standard approach to inference of hybridization networks is to (i) reconstruct rooted gene trees and (ii) leverage gene tree discordance for network inference. Recently, we introduced a method called RF-Net for accurate inference of virus reassortment and hybridization networks from input gene trees in the presence of errors commonly found in phylogenetic trees. While RF-Net demonstrated the ability to accurately infer networks with up to four reticulations from erroneous input gene trees, its application was limited by the number of reticulations it could handle in a reasonable amount of time. This limitation is particularly restrictive in the inference of the evolutionary history of segmented RNA viruses such as influenza A virus (IAV), where reassortment is one of the major mechanisms shaping the evolution of these pathogens. Results Here, we expand the functionality of RF-Net that makes it significantly more applicable in practice. Crucially, we introduce a fast extension to RF-Net, called Fast-RF-Net, that can handle large numbers of reticulations without sacrificing accuracy. In addition, we develop automatic stopping criteria to select the appropriate number of reticulations heuristically and implement a feature for RF-Net to output error-corrected input gene trees. We then conduct a comprehensive study of the original method and its novel extensions and confirm their efficacy in practice using extensive simulation and empirical IAV evolutionary analyses. Availability and implementation RF-Net 2 is available at https://github.com/flu-crew/rf-net-2. Supplementary information Supplementary data are available at Bioinformatics online.

Recently, many methods based on hand-designed convolutional neural networks (CNNs) have achieved promising results in automatic retinal vessel segmentation. However, these CNNs remain constrained in capturing retinal vessels in complex fundus images. To improve their segmentation performance, these CNNs tend to have many parameters, which may lead to overfitting and high computational complexity. Moreover, the manual design of competitive CNNs is time-consuming and requires extensive empirical knowledge. Herein, a novel automated design method, called Genetic U-Net, is proposed to generate a U-shaped CNN that can achieve better retinal vessel segmentation but with fewer architecture-based parameters, thereby addressing the above issues. First, we devised a condensed but flexible search space based on a U-shaped encoder-decoder. Then, we used an improved genetic algorithm to identify better-performing architectures in the search space and investigated the possibility of finding a superior network architecture with fewer parameters. The experimental results show that the architecture obtained using the proposed method offered a superior performance with less than 1% of the number of the original U-Net parameters in particular and with significantly fewer parameters than other state-of-the-art models. Furthermore, through in-depth investigation of the experimental results, several effective operations and patterns of networks to generate superior retinal vessel segmentations were identified. The codes of this work are available at https://github.com/96jhwei/Genetic-U-Net.

Feature selection is a significant task in the workflow of predictive modeling for data analysis. Recent advanced feature selection methods are using the power of optimization algorithms for choosing a subset of relevant features to get better classification results. Most of the optimization algorithms like genetic algorithm use many controlling parameters which need to be tuned for better performance. Tuning these parameter values is a challenging task for the feature selection process. In this paper, we have developed a new wrapper-based feature selection method called binary teaching learning based optimization (FS-BTLBO) algorithm which needs only common controlling parameters like population size, and a number of generations to obtain a subset of optimal features from the dataset. We have used different classifiers as an objective function to compute the fitness of individuals for evaluating the efficiency of the proposed system. The results have proven that FS-BTLBO produces higher accuracy with a minimal number of features on Wisconsin diagnosis breast cancer (WDBC) data set to classify malignant and benign tumors.

The electroencephalogram (EEG) has been commonly used to identify epileptic seizures, but identification of seizures from EEG remains a challenging task that requires qualified neurophysiologists. It is important to detect seizures in real time, which can be achieved in an internet of things (IoT)-based cloud platform to alert patients of impending seizures. Therefore, in this study, we propose a new model to recognize seizure states (e.g., ictal, preictal, interictal) from EEG in the IoT framework to monitor patients remotely. The proposed model uses an efficient hybrid genetic whale optimization algorithm (GWOA) based on naive Bayes (NB-GWOA) for feature selection, and an adaptive extreme learning machine (ELM) based on a differential evolutionary (DE) algorithm (DEELM) for classification. In the NB-GWOA method, the genetic algorithm serves to enhance the exploitation of the whale optimization algorithm in the search of the optimal solutions, while the naive Bayes method is used to determine a fitness function to assess every agent in the search space. GWOA has strong robustness and is capable of finding the best solutions in less than five iterations, so it is suitable for selecting discriminative features from a huge number of neurofeatures obtained from EEG. Further, the classification model is constructed based on ELM, which uses the DE algorithm for a fast and efficient learning solution. Results show that the proposed NB-GWOA-DEELM model can avoid over- and under-fitting and can provide better and more accurate performance in classifying seizure states from EEG than its competitors.

An optimal component feeder arrangement and robotic placement sequence are both important for improving assembly efficiency. Both problems are combinatorial in nature and known to be NP-hard. This article presents a novel discrete hybrid bat-inspired algorithm for solving the feeder slot assignment and placement sequence problem encountered when planning robotic assembly of electronic components. In our method, we use the concepts of swap operators and swap sequence to redefine position and velocity operators from the basic bat algorithm (BA). Furthermore, we propose an improved local search method based on genetic operators of crossover and mutation enhanced by the 2-opt search procedure. The algorithm is formulated with the objective of minimizing the total traveling distance of the pick-and-place device. Through numerical experiments, using a real printed circuit board (PCB) assembly scenario, we demonstrate the considerable effectiveness of the proposed discrete BA to improve the selection of feeder arrangement and placement sequence in PCB assembly operations and achieve high throughput production. The results also highlighted that even though the algorithms outperformed traditional lead through programming techniques, the programmer must consider the influence of different robot motions and speeds.

In this paper, we introduce a human-robot collaboration (HRC) mold assembly cell to cope with small-volume mold production and reduce the risk of musculoskeletal disorders (MSDs) on a human worker during manual mold assembly operation. Besides, the wide variety of types and weights of the mold components motivated us to design an HRC system that consists of two robots. Therefore, we propose two collaboration modes for HRC systems using two robots and develop a task-allocation model to demonstrate the application of these collaboration modes in the mold assembly. The task-allocation model assigns a task based on the task characteristics and capability of agents in the collaboration cell. First, we decompose the assembly operation into functional actions to analyze the characteristics of tasks. Then, we obtain the agent assignment preference based on task characteristics and capability of agents using the analytic network process. Finally, we apply the genetic algorithm in the final task allocation to minimize assembly time, use of a less capable agent, and ergonomic risk. This paper contributes to expanding the HRC system with two robots in the mold assembly to allow the execution of a greater diversity of tasks and improve the assembly time and MSD risk level for the human worker.

The ability of flying continuously over prolonged periods of time has become target of numerous research studies performed in recent years in both the fields of civil aviation and unmanned drones. High-altitude platform stations are aircrafts that can operate for an extended period of time at altitudes 17 km above sea level and higher. The aim of this paper is to design and optimize a wing for such platforms and computationally investigate its aerodynamic performance. For that purpose, two-objective genetic algorithm, class shape transformation and panel method were combined and used to define different airfoils with the highest lift-to-drag ratio and maximal lift coefficient. Once the most suitable airfoil was chosen, polyhedral half-wing was modeled and its aerodynamic performances were estimated using the computational fluid dynamics approach. Flow simulations of transitional flow at various angles-of-attack were realized in ANSYS FLUENT and various quantitative and qualitative results are presented, such as aerodynamic coefficient curves and flow visualizations. In the end, daily mission of the aircraft is simulated and its energy requirement is estimated. In order to be able to cruise above Serbia in July, an aircraft weighing 150 kg must accumulate 17 kWh of solar energy per day.

This work explores on-board planning for the single spacecraft, multiple ground station Earth-observing satellite scheduling problem through artificial neural network function approximation of stat...

With the continuous development of science and technology, the information age has arrived, and people have fully entered the information age. In this new era, Internet technology has achieved unprecedented development, and many innovative technologies have also been proposed. The competition in the logistics industry is becoming more and more fierce. Someone proposed the concept of intelligent logistics. This concept has been continuously expanded and the requirements have become higher and higher. Finally, an intelligent logistics management supported by the Internet of Things technology has emerged. technology. In our school's research, we mainly proposed an intelligent distribution model based on the Internet of Things. This model not only optimizes the distribution process, but also proposes an efficient distribution strategy when faced with a large amount of data. This technology mainly uses the information interaction technology in the Internet of Things, which can ensure that the average delivery speed is the fastest, the average transportation distance is the shortest, and the time consumed in the logistics transmission process is the shortest. In the decision-making process of this intelligent distribution model, we must first establish some intelligent distribution models controlled by multiple indicators. During the experiment, we built a logistics perception system and used heuristic algorithms to solve the packing problem. Answered. The mainstream solution algorithm we use genetic algorithm, a large number of experiments and data show that this algorithm is scientifically based, and it has also exerted its due effect in application. This article mainly analyzes the impact of the intelligent logistics system provided by the Internet of Things technology, and then looks forward to the future development trend of the system. We hope that through the analysis of the status quo, we can promote the further development of my country's Internet of Things technology, and also promote the further development and optimization of my country's intelligent logistics system technology.

In the solar cells under the open-circuit condition, the carriers charges emits the external luminescence for balancing the incoming solar radiations. The generation of such external luminescence represents the optical losses in the solar cells. Efficient external luminescence ensures the state of approaching the maximum limit of the SQ (Shockley-Queisser) equation. In this way in solar cell’s need of getting high efficiency is fulfilled if it is also responding as an efficient light-emitting diode. This article focus on achieving a high external luminescence efficiency under the standard solar spectrum with optimum value of fill factor using the optimum values of GaAs solar cell parameters by applying Genetic algorithm. Different geometries based on physics of light absorption are considered for optimum parameter search that can give high external luminiscence efficiency as well as high Value of fill factor.

A novel model called Instinct Plasticity Echo State Network with New Weights Selection Method, which is Optimized by Genetic Algorithm, is proposed (IP-NESN-GA). There are three proposed methods that are employed in the conventional ESN. New weights selection method provides a novel approach to replace the random weights of ESN, which obtains much better performance than the conventional ESN. At the same time, Instinct Plasticity is successfully applied in ESN, which enhances the connection among reservoir states and increase the accuracy. Finally, Genetic Algorithm is applied to seek the most suitable parameters. The detecting ability of our proposed model is assessed on two kinds of distinct grammatical constructions data. It not only performs significantly better than the baselines in the meaning error and sentence error, but it also visualizes the expected probabilities for each of the possible thematic roles.

Artificial Neural Network (ANN) is a form of computation inspired by the arrangement and purpose of the brain. Pattern Recognition is one of the demanding areas under Artificial Neural Network and pattern association is one of the major tasks through which pattern recognition is realized. Nowadays researchers are taking interest in the combination of evolutionary algorithms and ANN. In the present work, an attempt is being made to analyze the performance of Hopfield Associative Memory with Monte Carlo adaptation rule and evolutionary searching (Genetic Algorithm) for pattern storage and recall. The training pattern set has been taken a set of five Greek alphabet letters as different objects. The aim is to store Patterns with MC adaptation rule and get the best weight matrices for efficient recalling of any given set patterns. The neural networks architectures have been taken up for storing the given set of inputs, for which conventional Hebbian and Monte Carlo adaptation rule is considered. During the mutation process, various mutation probabilities are supposed to be evaluated. The detailed simulated results thus obtained will be presented with the help of tables and graph.

This study aims to solve the nonlinear fifth-order induction motor model (FO-IMM) using the Gudermannian neural networks (GNNs) along with the optimization procedures of global search as a genetic algorithm together with the quick local search process as active-set technique (GNN-GA-AST). GNNs are executed to discretize the nonlinear FO-IMM to prompt the fitness function in the procedure of mean square error. The exactness of the GNN-GA-AST is observed by comparing the obtained results with the reference results. The numerical performances of the stochastic GNN-GA-AST are provided to tackle three different variants based on the nonlinear FO-IMM to authenticate the consistency, significance and efficacy of the designed stochastic GNN-GA-AST. Additionally, statistical illustrations are available to authenticate the precision, accuracy and convergence of the designed stochastic GNN-GA-AST.

Soft Computing is the study of thinking, propositioning and derivations. The thought behind soft computing is to display psychological conduct of human brain. Soft computing is establishment of reasonable insight in machines. In contrast to hard registering, soft computing is open minded toward imprecision, vulnerability, incomplete truth and estimation. The job model for soft computing is the human brain. The core value of soft computing is: Exploit the capacity to bear imprecision, vulnerability and fractional truth to accomplish manageability, strength and low arrangement cost. Soft computing techniques are now been utilized as a tool for solving real-life day-to-day problems. Soft computing procedures are being preferred because of the unpredictability and vulnerability of the various processes. Major soft computing tools applied for this object are genetic algorithm, ant colony optimization, swarm intelligence, and particle swarm optimization. The primary point of this paper is to sum up a portion of the streamlining methods for acknowledging and using its application to dominate insightful work.

To overcome the limitations of pavement skid resistance prediction using the friction coefficient, a genetic-algorithm-improved neural network (GAI-NN) was developed in this study. First, three-dimensional (3D) point-cloud data of an asphalt pavement surface were obtained using a smart sensor (Gocator 3110). The friction coefficient of the pavement was then obtained using a pendulum friction tester. The 3D point-cloud dataset was then analyzed to recover the missing data and perform denoising. In particular, these data were filled using cubic spline interpolation. Parameters for texture characterization were defined, and methods for computing the parameters were developed. Finally, the GAI-NN model was developed by modifying the weights and thresholds. The test results indicated that using pavement surface texture 3D data, the GAI-NN was capable of predicting the pavement friction coefficient with sufficient accuracy, with an error of 12.1%.

Accurate mapping urban impervious surfaces (UIS) from satellite images is crucial for understanding urban land cover change directions and addressing UIS change-induced environmental issues. The UIS index is an effective way to reduce the computational cost of processing remote sensing imageries although continuous UIS detection from multitemporal Landsat images, repeated fine-tuning, and reselection for a proper threshold should be considered. In this article, we proposed a novel UIS index and an improved particle swarm optimization (PSO)-based UIS automatic threshold selection model (UISAT) to overcome those shortages when detecting UIS derived from multitemporal Landsat images. The proposed UIS index transformed different temporal Landsat images into index images with distinguishable pixel values for each gray pixel. In this way, the pixel gray values representing UIS were enhanced, and non-UIS was weakened with lower gray scales in the transformed index images. Next, an automatic threshold selection strategy was developed to enhance the applicability of the PSO model in terms of threshold fine-tuning and reselection. Then, we performed comprehensive assessments for the UISAT model, including in comparison to an improved convolutional deep belief network for relative accuracy verification, random points matching with Google Earth images, and comparing to other typical indices. The results demonstrated that the UISAT model provided the best performance in UIS detection from multitemporal Landsat images. The producer's accuracies (PAs) of UISAT results in Beijing, Hefei, and Guangzhou, China, were 91.73%, 93.87%, and 92.84%, while the user's accuracies (UA) were at 91.51%, 93.35%, and 92.24%. The proposed model outperformed the existing commonly used UIS indices [e.g., the Biophysical Composition Index (BCI), the Combinational Build-up Index (CBI), the Genetic Algorithm-based Urban Cluster Automatic Threshold (GA-UCAT), and the Gaussian-based Automatic Threshold Modified Normalized Difference Impervious Surface Index (G-MNDISI)] by an average increase of 6.51% in overall accuracy.