Snake Algorithm Research Articles

Gated Graph Attention-based Crossover Snake (GGA-CS) Algorithm for Hyperspectral Image Classification

Gated Graph Attention-based Crossover Snake (GGA-CS) Algorithm for Hyperspectral Image Classification

Combining dynamic adaptive snake algorithm with perturbation and observation for MPPT in PV systems under shading conditions

Photovoltaic (PV) systems play an increasingly vital role in the global new energy landscape. However, when PV arrays are affected by partial shading, their power-voltage (P-U) characteristic curves exhibit dynamic multi-peak features, posing challenges to traditional Maximum Power Point Tracking (MPPT) techniques, including slow convergence speed, low tracking efficiency, and oscillations around the Maximum Power Point (MPP), leading to unnecessary power losses. Therefore, this study proposes a dual-layer control MPPT technology that integrates dynamic adaptive snake optimization algorithm with variable step perturbation and observation (ISO-IP&O) to address the MPP extraction problem of PV systems under complex weather conditions. This study compares and analyzes the proposed ISO-IP&O controller with current renowned MPPT techniques, including Grey Wolf Optimization combined with P&O (GWO-P&O), Improved Squirrel Search Algorithm (ISSA), Original Snake Optimization (SO), Horse Herd Optimization (HHO), and Adaptive Factor Selection Multi-Swarm Particle Swarm Optimization (FMSPSO), validating the effectiveness of the proposed ISO-IP&O controller. Simulation results demonstrate that the ISO-IP&O technology achieves an average tracking efficiency of 99.91 % under various shading conditions, with an average convergence speed of only 0.07 seconds. Compared with other existing methods, the ISO-IP&O technology exhibits superior performance in terms of GMPP tracking speed, maximum power tracking efficiency, and stability. Experimental validation on the HIL+RCP physical experimental platform shows that under eight partial shading conditions(PSC), the proposed method still achieves the highest average tracking efficiency of 99.68 %, with the fastest average tracking speed of 0.66 seconds. The ISO-IP&O controller achieves rapid, robust, and efficient GMPP tracking under various complex shading conditions, contributing to the enhancement of PV system performance.

Detection of Blood Cancer Cells using Microscopic Image

For the automatic diagnosis and classification of leukemia and leukemoid reactions, the IDB2 (acute lymphoblastic leukemia-image database) dataset has been utilized. This paper focuses on an automated method to differentiate between leukemoid and leukemia reactions using images of blood smear. MobileNetV3 is employed to classify and count WBC types from segmented images. The BCCD (Blood Cell Count Detection) dataset, which contains 364 images of blood smear and 349 single WBC type images, has been used in this work. The image segmentation algorithm incorporates Fuzzy C-means clustering, the snake algorithm, and fusion rules. For classification, the VGG19 CNN (Convolutional Neural Network) architecture-based deep learning technique is implemented. Python and Google colab is used for designing and executing the algorithm respectively.

A simulation‐based approach for optimizing the placement of dedicated lanes for autonomous vehicles in large‐scale networks

AbstractThis study introduces a framework to maximize societal benefits associated with the autonomous vehicle (AV)‐dedicated lane implementation at large‐scale transportation networks, considering the travel time savings and the required investments to prepare the infrastructure for their deployment. To this end, a bi‐level optimization problem is formulated. The upper level determines the links for dedicated lane deployment, while at the lower level, a mesoscopic traffic simulation tool is employed to enable a realistic representation of these vehicles in a mixed traffic. The problem is solved using the genetic algorithm. To further reduce the computational burden, this study adopts a clustering method based on the snake algorithm to group the candidate links and reduce the size of the solution space. The proposed framework is successfully applied to the case study of Chicago downtown network, considering various demand levels, AV market penetration rates, and implementation approaches. The results highlight the need for optimizing the placement of AV‐dedicated lanes (AVDLs) to ensure the economically beneficial adoption of this strategy across different scenarios. This study provides transportation planners with key operational insights to facilitate the effective adoption of AVDLs during the transitional phase from human‐driven vehicles to a fully AV environment.

Generating grid maps via the snake model

Generating grid maps via the snake model

Gaussian model for closed curves

In the case of image processing or understanding, one of the common important tasks is to fit closed curves (e.g., circles, ellipses, etc.) to the underlying image. In higher-dimensional situations, the problem of modeling clusters as closed curves remains even more challenging.To deal with this problem we introduce a new probability distribution, which models complicated closed curves with the use of Fourier series. Then, a mixture of such distributions is constructed, leading to our model MCEC. It is shown that MCEC can be effectively trained in the case of closed curves in Rn.MCEC was evaluated in clustering, curve fitting, and image segmentation tasks. We compare it in particular to classical GMM, Hough transform, and Snakes algorithm. In each task, we consistently obtain or outperform the current SOTA.

Adaptive SPP–CNN–LSTM–ATT wind farm cluster short-term power prediction model based on transitional weather classification

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather.

Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region

Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.

Using the snake optimization metaheuristic algorithms to extract the photovoltaic cells parameters

The accurate extraction of photovoltaic cells and panels parameters is very important in order to precisely forecast their generated energy, to rapidly identify the maximum power point, both for the manufacturer in order to verify the quality control during the production process, and for the researchers to improve their quality, efficiency and also study the photovoltaic cells degradation. This paper is the first to propose the usage of the Snake optimization metaheuristic algorithms to extract the parameters of three photovoltaic cells: monocrystalline commercial silicon, amorphous silicon, and RTC France. The Snake algorithms are used for the one diode and two diode models to extract five and seven parameters respectively. The root mean square error statistical test has been performed in order to prove the algorithm performance. The comparison with the algorithms published in specialized literature reveals that the Snake algorithm outperforms several others, but is not the best. An improved version is developed and the results show that it gives better results than, or at least the same as the best ones. The improvement in root mean square error, when the improved snake algorithm is used, is 16% in the case of the monocrystalline silicon photovoltaic cell for the two diode model, 5.3% in the case of the amorphous silicon photovoltaic cell for the two diode model, and it is slightly better for the RTC France photovoltaic cell, from 0.002 to 0.11% when the best algorithms are considered. The Snake algorithms give very good results, especially the improved one, even for a small population size and a limited number of iterations. Thus, for the improved snake algorithm, the iterations number and the population size are 100 and 80, in the case of the monocrystalline photovoltaic cells, for one diode model, and 200 and 40 respectively for two diode model, 100 and 120 in the case of the amorphous silicon photovoltaic cell for one diode model and 120 and 120 respectively for two diode model, 150 and 100 in the case of RTC photovoltaic cell for one diode model and 150 and 80 respectively for two diode model. This reduces the computational time for the extraction of the photovoltaic cell parameters. The computational time for the improved snake algorithm is half of the necessary one for one of the best algorithms, the hybrid successive discretization algorithm. Therefore, the snake optimization algorithm, especially the improved one is one of the best algorithms that can be used for the parameters extraction of the photovoltaic cells.

Automatic Segmentation and Mechanical Characterisation of the Intraluminal Thrombus and Arterial Wall of Abdominal Aortic Aneurysms Using Time Resolved 3D Ultrasound Images

This study proposes a method for semi-automatic segmentation of abdominal aortic aneurysms (AAAs) and their intraluminal thrombus (ILT), based on time resolved 3D ultrasound (US), and validates results with computed tomography (CT). Moreover, mechanical properties of both wall and ILT are determined, and possible correlations with ILT size and blood pressure are investigated. A semi-automatic segmentation algorithm was developed combining a star-Kalman approach with a 3D snake algorithm. The segmented geometries of both lumen and inner vessel wall were validated with both manual US based segmentations and CT based segmentations. Finally, the lumen and vessel wall distensibility and ILT compressibility were estimated, and correlated to ILT size and blood pressure. For the vessel wall and lumen, the median Similarity Index (SI) was 92% (IQR 90, 94%) and 83% (IQR 75, 87%), respectively. The distensibility of the vessel wall could be determined in 37 out of 40 cases, and had a median value of 0.28 10-5 Pa-1 (IQR 0.18, 0.51⋅10-5). The median systolic-diastolic volume change of the ILT was determined successfully in 21 out of 40 patients, and was -0.57% (IQR -1.1, 1.2%). The vessel and lumen distensibility showed a strong correlation with the systolic pressure (p < .010), rather than with the diastolic pressure. Besides, the lumen distensibility was strongly correlated with the ILT thickness (p = .023). The performance of the semi-automatic segmentation algorithm was shown to be as good as the manual segmentations and highly dependent on the visibility of the ILT (limited contrast in seven patients and clutter in nine patients). This study shows promising results for mechanical characterisation of the vessel, and ILT, including a correlation between distensibility, ILT size, and blood pressure. For future work, inclusion rate needs to be increased by improving the image contrast with novel US techniques.

Smoothing and compressing algorithm of toolpath with complex contour in NC machining

The continuous short line segment processing method in CNC machining has the defect of frequent fluctuation of feedrate, which affects the quality and efficiency. For this problem, the local and global methods are common solutions. The local method is to insert transition curves at the corners to obtain a smooth toolpath, but this method does not essentially reduce the number of accelerations and decelerations, and it is difficult to insert transition curves between tiny straight sections. Although the global method can obtain smooth tool trajectories, the parametric interpolation method is not suitable for middle- and low-end CNC machine tools. The article proposes an adaptive tool path generation method with the original part contour as the research object and discretizes the NURBS curves into smooth trajectories consisting of large segments of straight lines and circular arcs. The article mimics the crawling characteristics of snakes and designs a double-headed snake algorithm based on the least squares method to implement this process. First, given the start and end points of the curve, two snakeheads search for the maximum linear and circular segments that satisfy the error constraint in the curve direction. The winner will then be selected as the candidate track segment through a competition mechanism. Finally, all trajectory segments are connected to obtain a smooth tool path. Experiments show that the toolpath data of the method in this paper are reduced by 75.56% compared with commercial CAM software when the contour error threshold is the same, and the contour error is reduced by more than one order of magnitude when the number of toolpath segments is equal. In addition, the method in this paper can obtain a smoother machining surface and more stable cutting force, thus achieving a win‒win situation of quality and efficiency.

A NOVEL APPROACH TO INTRUSION-DETECTION SYSTEM: COMBINING LSTM AND THE SNAKE ALGORITHM

In the epoch of digital transformation, cloud computing remains paramount, acting as the linchpin for a plethora of services from enterprise solutions to day-to-day consumer applications. Yet, its expansive nature has invariably rendered it susceptible to a myriad of cyber threats, necessitating advanced, adaptive defense mechanisms. This paper introduces a novel intrusion detection method tailored for cloud environments, ingeniously amalgamating the temporal pattern recognition capabilities of Long Short-Term Memory (LSTM) networks with the heuristic finesse of the Snake algorithm. Our research meticulously delineates the LSTM-Snake model’s design, implementation, and exhaustive benchmarking against prevailing approaches. Experimental results underscore the model’s prowess, registering a commendable 99% accuracy rate in intrusion detection—a marked improvement over current state-of-the-art methodologies. The ensuing discussions offer insights into the model’s practical implications, potential limitations, and avenues for future research, paving the way for a fortified cloud computing landscape

Detection and diagnosis of skin diseases by using snake algorithm and neural networks

This research presents a medical application to diagnose types of skin diseases based on the axis of dermatology, where a computer intelligent system was built based on identifying and diagnosing the type of skin inflammation, and this system called (SANN) (Snake Algorithm Neural Network). The system consists of two stages, the first stage is the axis of locating the presence of skin inflammation and distinguish it from uninfected skin by performing the initial processing of the image using the snake algorithm. while the second stage using neural networks to diagnose types of skin disease that were identified in the first stage by adding some improvements to the neural networks to work more efficiently for diagnosis. The suggested SANN system was applied to 250 images, and the accuracy and execution time were calculated. The results showed that using the system based on the snake algorithm and neural networks in the process of identifying and diagnosing types of dermatitis (psoriasis or Spider birthmark) achieves high performance and accuracy, and gave a diagnosis rate of 88.9%

Improved Active Contour Snake Model for Hollow Filter Bar Roundness Inspection

Cigarette filter rods are a kind of easily deformed and irregularly shaped parts, and the quality of cigarette filter rods is often measured by the filter rod roundness index, although they are affected by factors such as environment and humidity, which can affect the detection of filter rod roundness and show blurred images. For the detection of blurred filter rod image roundness, an improved active contour snake model algorithm is proposed. The experimental results show that in the analysis of the algorithm iteration number on the model performance, the model performance gradually leveled off at the iteration number of 200. Comparing the PR of the improved algorithm with the traditional snake algorithm with the balloon force snake algorithm, the accuracy and recall rates obtained were 0.803 and 0.798, respectively, which were greater than the other two algorithms; the error of the value of the residual under the improved snake algorithm was always smaller than the other algorithms, which indicated that the convergence effect of the improved snake algorithm on the error of filter bar roundness was better than the other experimental algorithms. The abovementioned results indicate that the improved snake algorithm has high feasibility and effectiveness in the application of filter bar roundness error detection and has a significant value.

Early Detection of Obstacle to Optimize the Robot Path Planning

Robot path planning is one of the core issues in robotics and its application. Optimizing the route discovery becomes more important while dealing with the robot-based application. This paper proposes the concept of early detection of the obstacle present in the workspace of the robots. To early detect the obstacle, this paper proposes the concept of a snake algorithm along with the traditional path planning algorithms. The contour detection part is merged with the different path planning algorithms to optimize the robot traversing and benefit it in producing good results. Obstacle-free optimized path is one of the core requirements for robots in any application. With the help of path planning algorithms, robots are enabled to derive those paths in a specific environment. The presence of an obstacle makes it difficult for any path planning algorithms to derive a smooth path. The purpose of using the snake algorithm is to detect an obstacle early. This method not only perceives the obstacle but also catches out the complete boundary of the obstacle, it, thus, provides the details of obstacle coordinates to the path planning algorithm. Conceiving the complete periphery of obstacles can have multiple advantages in many application areas. A*, PRM, RRT, and RRT Smooth algorithms are considered along with the snake algorithm to validate our work in three different experimental scenarios: Maze, Random Obstacles, and Dense case. Path length, Time-taken, and Move count are parameters taken to observe the results. The result obtained using the snake algorithm with four path planning algorithms is analyzed and compared in detail with the core A*, PRM, RRT, and RRTS. Finally, the result obtained using the proposed methodology gives some encouraging results and also predicts the exploration of the robot’s path planning for more applications and fields.

The estimation of sea-breeze front velocity over coastal urban using Himawari-8 images: A case study in Jakarta

&lt;p&gt;&lt;em&gt;The sea breeze is a meteorological phenomenon that occurs due to the contrast temperature between land and oceans. The propagation velocity of sea breeze are influenced strongly by e.g., synoptic wind and geographical conditions. Therefore, it is important to understand the relationship between the spatial distribution of sea breeze velocity and the surface characteristic, for instance over urbanized and less-urbanized coastal areas. When the sea breeze propagates inland, a cumulus cloudline will form in the vicinity of the sea breeze front (SBF). Previous studies have successfully detected the cloudline automatically using the morphological-snake algorithm. In this paper, we estimate the SBF velocity using Himawari-8 satellite images. The proposed method segmented the cloudline data points using a clustering approach, named machine learning-based k-means++, on the level-set obtained from snake algorithm. We then estimate the SBF velocity by calculating the haversine distance of the segmented cloudline points that propagate over time. The comparison of estimated cloudline speed with SBF speed measured at two observation sites, namely KKP and BPL, reveals the root mean square errors 1.39 m/s and 1.41 m/s, respectively. And the propagation direction was mainly southward.&lt;/em&gt;&lt;/p&gt;

A Framework for Sea Breeze Front Detection from Coastal Regions of India Using Morphological Snake Algorithm

Sea breezes are the most common winds experienced by people living in coastal regions. In general, the sea breeze is a flow of winds that particularly takes place in coastal areas. Sea breezes cause irregular climatic conditions. The salty air coming from the sea breeze has got many adverse effects like deterioration. The collision of two powerful sea breezes fronts can cause severe thunderstorms across the coastal regions. Therefore, it is important to know the location of the sea breeze front to define the regions affected by sea breezes. In order to detect the sea breeze front from satellite images, it is important to segment the satellite images. Image segmentation which helps in extracting the objects of interest and make the image more meaningful for further processing. Then using contour detection, the outline of sea breeze, which is sea breeze front can easily be extracted. A proper methodology with user interface is proposed in this paper for detecting the sea breeze front from the satellite images.

Design and implementation of a cost effective vacuum cleaner robot

Robot vacuum cleaners are among the first service robots to enter daily life. However, robot vacuum cleaners are currently inaccessible to many due to their high cost. In order for these robots to become widespread, they must be cheap and functional. In this study, the design and production of a low-cost, high-performance vacuum cleaner robot that can be controlled by smartphones is presented. This autonomous robot moves around the obstacles, vacuums the dust from the floor, performs basic navigation and also it is manually controllable. Designing of a vacuum cleaner robot is divided into three parts which are the mechanical, electrical and software design. In the mechanical design; the robot chassis and cleaning system have been designed and realized. In the electrical design; the electrical components such as batteries, motors, drivers, sensors and microcontroller have been selected and the power system has been designed by considering electrical requirements. In the software design; an easy to use Android application for remote control has been created, autonomous algorithms such as random walk and snake algorithms have been designed. Remote controlling of the robot has been provided by using Bluetooth connection between the Raspberry Pi and Android smartphone.

Edge Segmentation Algorithm‐Based Prevention of Recurrent Laryngeal Nerve Injury in Treatment of Thyroid Nodules Using Ultrasound Images

The study was aimed to explore the segmentation effects of different algorithms on thyroid nodule ultrasound images, so as to better protect the recurrent laryngeal nerve during thyroid surgery. Specifically, 186 patients with thyroid nodules were selected as the research objects. The segmentation performances of the gradient vector flow (GVF) Snake, Watershed, and Snake algorithms were compared from 6 aspects of image segmentation effects, pixel accuracy (PA), Intersection over Union (IOU) value, algorithm running time, postoperative recurrent laryngeal nerve injury, intraoperative bleeding volume, and postoperative drainage volume. It was found that the average PA value (0.954) and the IOU value (0.866) of the GVF Snake algorithm were obviously higher than those of the other two algorithms. The total incidence of recurrent laryngeal nerve injury based on the GVF Snake algorithm (4.69%) was obviously lower than that of the Snake algorithm (19.35%) and the watershed algorithm (16.13%). The bleeding volume and postoperative drainage volume based on the GVF Snake algorithm were less versus the other two algorithms ( P &lt; 0.05 ). In conclusion, the GVF Snake algorithm demonstrates ideal segmentation effects, which is suggested in the treatment of thyroid nodules to better protect the recurrent laryngeal nerve.

Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia

The impact of floods is the most severe among the natural calamities occurring in Malaysia. The knock of floods is consistent and annually forces thousands of Malaysians to relocate. The lack of information from the Ministry of Environment and Water, Malaysia is the foremost obstacle in upgrading the flood mapping. With the expeditious evolution of computer techniques, processing of satellite and unmanned aerial vehicle (UAV) images for river hydromorphological feature detection and flood management have gathered pace in the last two decades. Different image processing algorithms—structure from motion (SfM), multi-view stereo (MVS), gradient vector flow (GVF) snake algorithm, etc.—and artificial neural networks are implemented for the monitoring and classification of river features. This paper presents the application of the k-means algorithm along with image thresholding to quantify variation in river surface flow areas and vegetation growth along Kerian River, Malaysia. The river characteristic recognition directly or indirectly assists in studying river behavior and flood monitoring. Dice similarity coefficient and Jaccard index are numerated between thresholded images that are clustered using the k-means algorithm and manually segmented images. Based on quantitative evaluation, a dice similarity coefficient and Jaccard index of up to 97.86% and 94.36% were yielded for flow area and vegetation calculation. Thus, the present technique is functional in evaluating river characteristics with reduced errors. With minimum errors, the present technique can be utilized for quantifying agricultural areas and urban areas around the river basin.