Local Point Research Articles

Selective image segmentation driven by region, edge and saliency functions

Present active contour methods often struggle with the segmentation of regions displaying variations in texture, color, or intensity a phenomenon referred to as inhomogeneities. These limitation impairs their ability to precisely distinguish and outline diverse components within an image. Further some of these methods employ intricate mathematical formulations for energy minimization. Such complexity introduces computational sluggishness, making these methods unsuitable for tasks requiring real-time processing or rapid segmentation. Moreover, these methods are susceptible to being trapped in energy configurations corresponding to local minimum points. Consequently, the segmentation process fails to converge to the desired outcome. Additionally, the efficacy of these methods diminishes when confronted with regions exhibiting weak or subtle boundaries. To address these limitations comprehensively, our proposed approach introduces a fresh paradigm for image segmentation through the synchronization of region-based, edge-based, and saliency-based segmentation techniques. Initially, we adapt an intensity edge term based on the zero crossing feature detector (ZCD), which is used to highlight significant edges of an image. Secondly, a saliency function is formulated to detect salient regions from an image. We have also included a globally tuned region based SPF (signed pressure force) term to move contour away and capture homogeneous regions. ZCD, saliency and global SPF are jointly incorporated with some scaled value for the level set evolution to develop an effective image segmentation model. In addition, proposed method is capable to perform selective object segmentation, which enables us to choose any single or multiple objects inside an image. Saliency function and ZCD detector are considered feature enhancement tools, which are used to get important features of an image, so this method has a solid capacity to segment nature images (homogeneous or inhomogeneous) precisely. Finally, the adaption of the Gaussian kernel removes the need of any penalization term for level set reinitialization. Experimental results will exhibit the efficiency of the proposed method.

Texture characterization and classification of polarized images based on multi-angle orthogonal difference.

The Local Binary Pattern (LBP) and its variants are capable of extracting image texture and have been successfully applied to classification. However, LBP has not been used to extract and describe the texture of polarized images, and simple LBP cannot characterize the polarized texture information from different polarizations of angles. In order to solve these problems, we propose a new multi-angle orthogonal difference polarization image texture descriptor (MODP_ITD) by analyzing the relationship between the difference of orthogonal difference polarization images from different angles and the pixel intensity distribution in the local neighborhood of images from different angles. The MODP_ITD consists of three patterns: multi-angle polarization orthogonal difference local binary pattern (MODP_LBP), multi-angle polarization orthogonal difference local sampling point principal component sequence pattern (MODP_LPCSP) and multi-angle orthogonal difference polarization local difference binary pattern (MODP_LDBP). The MODP_LBP extracts local corresponding texture characteristics of polarized orthogonal difference images from multiple angles. The MODP_LPCSP sorts the principal component order of each angle orthogonal difference local sampling point. The MODP LDBP extracts the local difference characteristics between different angles by constructing a new polarized image. Then, the frequency histograms of MODP_LBP, MOD_LPCSP ,and MODP_LDBP are cascaded to generate MODP_ITD, so as to distinguish local neighborhoods. By using vertical and parallel polarization and unpolarized light active illumination, combined with the measurements at three different detection zenith angles, we constructed a polarization texture image database. A substantial number of experimental results on the self-built database show that our proposed MODP_ITD can represent the detailed information of polarization images texture. In addition, compared with the existing LBP methods, The MODP_ITD has a competitive advantage in classification accuracy.

Network Routing Optimization Using Tabu Search Algorithm in Dynamic Routing

Internet penetration is increasing along with the need for data packages for communication such as social media, chatting, video conferencing and others. On large-scale networks such as the Internet, dynamic routing is used to build routing protocol information in the routing table automatically. Currently, Djikstra's algorithm is used to solve the shortest path problem in dynamic routing. In this research, the optimization of the algorithm is carried out in determining the best path or trajectory. One of the optimization algorithms is the Tabu Search Algorithm which can guide heuristic local search procedures to explore the solution area outside the local optimum point. This optimization is assessed from the test parameters measured from the smallest cost. The data analyzed is in the form of bandwidth and topological flow. From the results of tracing the path of data packets sent through 9 routers using the Tabu Search algorithm with the parameters namely number of Neighbor Solutions = 50, Length of tabu list = 10, Maximum Number of Iterations = 100, the result of the path matrix value is 180.9676. The path taken is router 0-2-4-8-9

Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection

Computer systems store massive amounts of data with numerous features, leading to the need to extract the most important features for better classification in a wide variety of applications. Poor performance of various machine learning algorithms may be caused by unimportant features that increase the time and memory required to build a classifier. Feature selection (FS) is one of the efficient approaches to reducing the unimportant features. This paper, therefore, presents a new FS, named BDE-BSS-DR, that utilizes Binary Differential Evolution (BDE), Binary Stochastic Search (BSS) algorithm, and Dimensionality Reduction (DR) mechanism. The BSS algorithm increases the search capability of the BDE by escaping from local optimal points and exploring the search space. The DR mechanism then reduces the dimensions of the search space gradually. As a result of using DR, the local optima of the search space and the problem of wrong removal of important features before starting the search process are reduced. The algorithm's efficiency is evaluated on 20 different medical datasets. The obtained outcomes indicate that the BDE-BSS-DR outperforms the BDE and BDE-BSS algorithms significantly. Furthermore, the effectiveness of the proposed algorithms in selecting the most important features of the heart disease data, several cancer diseases, and COVID-19 are also compared with several other state-of-the-art methods. Our results show that the BDE-BSS-DR with SVM classifier has a significant advantage over other methods with an average classification accuracy of 95.05% in heart disease and 99.40% in COVID-19 disease. In addition, the comparisons made with KNN and SVM classification prove the efficiency of the DR and BSS in generating a subset of optimal and informative features.

Rate-induced tipping in complex high-dimensional ecological networks.

In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time. Depending on how fast such a parameter changes, a tipping point can occur. Existing works on rate-induced tipping, or R-tipping, offered a theoretical way to study this phenomenon but from a local dynamical point of view, revealing, e.g., the existence of a critical rate for some specific initial condition above which a tipping point will occur. As ecosystems are subject to constant disturbances and can drift away from their equilibrium point, it is necessary to study R-tipping from a global perspective in terms of the initial conditions in the entire relevant phase space region. In particular, we introduce the notion of the probability of R-tipping defined for initial conditions taken from the whole relevant phase space. Using a number of real-world, complex mutualistic networks as a paradigm, we find a scaling law between this probability and the rate of parameter change and provide a geometric theory to explain the law. The real-world implication is that even a slow parameter change can lead to a system collapse with catastrophic consequences. In fact, to mitigate the environmental changes by merely slowing down the parameter drift may not always be effective: Only when the rate of parameter change is reduced to practically zero would the tipping be avoided. Our global dynamics approach offers a more complete and physically meaningful way to understand the important phenomenon of R-tipping.

4D RADAR/IMU/GNSS INTEGRATED POSITIONING AND MAPPING FOR LARGE-SCALE ENVIRONMENTS

Abstract. 4D millimeter-wave radar can work in harsh weather conditions such as fog and snow, and measure the position and radial Doppler velocity of objects in three-dimensional space. Some existing methods perform positioning by fusing 3D velocity or point cloud matching information estimated by 4D radar with IMU information. However, the sparsity of radar point clouds and the interference of large amount of noise lead to low accuracy of odometry, and most of the existing work cannot achieve the global consistent mapping with radar point clouds. In this paper, a 4D radar/IMU/GNSS localization and mapping method, G-iRIOM, is proposed. We tightly coupled IMU measurement, Doppler observation information and point cloud matching information from 4D radar by iterative extended Kalman filtering (iEKF) method, and introduced GNSS RTK and loop closure information as global observation to correct the positioning drift of the odometry. The experimental results show that the tightly coupled Doppler velocity information can effectively improve the control of the pose on the local point cloud position, thus enhancing the mapping accuracy. Meanwhile, the introduction of GNSS and loop closure information can significantly improve the positioning accuracy of 4D radar odometry as a kind of global observation, and achieve global consistent mapping of large-scale outdoor scenes based on 4D radar point clouds.

A novel cell phone localization solution for trapped victims based on compressed RSSI fluctuation range and PSO-BP neural network

With the frequent occurrence of global earthquake disasters in recent years, quickly and accurately locating trapped people is the key to reducing the mortality rate of earthquake disasters. This paper proposes a cell phone localization solution for trapped people that can be rapidly deployed after an earthquake. This solution locates the buried cell phone by collecting its received WiFi signal strength indication (RSSI) data. Reducing the fluctuation of RSSI value is crucial to accurately locating the buried cell phone. We filter RSSI data outliers by hybrid filtering and propose a fluctuation range compression factor Q to reduce the fluctuation range of RSSI data. In order to adapt to the compressed RSSI data fluctuation range, the processed RSSI data were grouped. The grouped data are then imported into the PSO-BP neural network training to build the ranging model. So that there is a mapping relationship between RSSI data and trapped distance. The established ranging model is denoted as the HFQBP ranging model. After establishing the ranging model, the cell phone is localized by an Optimized Multilateration (OM) algorithm. For simplicity, this algorithm is named HFQBP-OM. In the experimental phase, we combined the method proposed in this paper with other methods, resulting in three different localization algorithms: HFQBP-WSC, PL-OM, and PL-WSC. And compared with the algorithms in this paper. The results indicate that the algorithm HFQBP-OM proposed in this paper has improved the total average accuracy of the nine localization points by 34.7%, 54.8%, and 58.9% compared to HFQBP-WSC, PL-OM, and PL-WSC, respectively. The HFQBP-OM algorithm has higher accuracy and stability. Also, it should be noted that the localization solution proposed in this paper uses smartphones connected to WiFi probes to collect RSSI data of the WiFi signal of the buried cell phone. Use a laptop computer to solve the localization coordinates. Smartphones and laptops are so popular that WiFi probes are the only special equipment needed. WiFi probe is inexpensive. It reduces the difficulty of implementing the localization solution in this paper and increases the generalizability of the solution.

An early indicator index of tornadic storms for Euro-Mediterranean region

Tornadoes are the most violent and destructive of all the severe weather phenomena that localized convective storms produce. There is a requirement in operational meteorology increasing nowadays that an indicator index which allows to reduce the uncertainty of severe convective storms and tornadoes in the scope of climate change adaptation strategies. The main intention is not to replace or substitute mesoscale modeling approaches, or composite indexes, but to warn operationally to draw attention to the Eastern Mediterranean and Türkiye in particular a few days in advance. The development of some indicators using atmospheric variables can undertake a crucial role by enabling such numerical models to be run only at certain time intervals, thus enduring lower computational costs. In this study, Eastern Mediterranean oscillation index (EMEDOi) has been developed in order to be able to detect the presence of ULLs (upper-level low) and frontogenesis approach is employed for selected tornadic storm events in Türkiye. EMEDOi has 7 different its variations (members) which these members have been developed to detect differences depending on the entry directions of cyclones and storms influencing Türkiye from the west of the country. In line with the GDAS data analysis, values of geopotential height are derived for the requirement of EMEDOi in a limited area. A few of the results from the study are as in the following: 86% of the trained tornado events revealed that the EMEDO-Oper index was in negative phase at the time a tornado was reported, regardless of whether the events featured a supercell mesoscale convective storm or a frontal movement. The hourly period until the local minimum is obtained can be described and characterized as the process by which the EMEDO-Oper index value decreases continuously. The time required to reach the local minimum varies based on the tornado occurrence. Based on the tornadic storm scenario in the test cluster in 2022 and the train cluster, this timeframe is predicted to be roughly 33.2 h on average. In western Türkiye, there is a 79% chance of a tornado occurring between six and forty-two hours after the EMEDO-Oper index reaches its local minimum. In particular, the projected chance for this period is 63% between 12 and 30 h after the local minimum is obtained. Besides, the majority of the tornado incidents with EMEDO-Oper values below − 0.75 were evaluated. After an EMEDO-Oper index value falls below that threshold, it is likely to forecast the risk period of a tornado in Türkiye with a probability of 79% and the local minimum point must be identified.

Background Influence of PM2.5 in Dallas–Fort Worth Area and Recommendations for Source Apportionment

Source apportionment of observed PM2.5 concentrations is of growing interest as communities seek ways to improve their air quality. We evaluated publicly available PM2.5 data from the USEPA in the Dallas–Fort Worth metropolitan area to determine the contributions from various PM2.5 sources to the total PM2.5 observed. The approach combines interpolation and fixed effect regression models to disentangle background from local PM2.5 contributions. These models found that January had the lowest total PM2.5 mean concentrations, ranging from 5.0 µg/m3 to 6.4 µg/m3, depending on monitoring location. July had the highest total PM2.5 mean concentrations, ranging from 8.7 µg/m3 to 11.1 µg/m3, depending on the location. January also had the lowest mean local PM2.5 concentrations, ranging from 2.6 µg/m3 to 3.6 µg/m3, depending on the location. Despite having the lowest local PM2.5 concentrations, January had the highest local attributions [51–57%]. July had the highest mean local PM2.5 concentrations, ranging from 2.9 µg/m3 to 4.1 µg/m3, depending on the location. Despite having the highest local PM2.5 concentrations, July had the lowest local attributions [33–37%]. These results suggest that local contributions have a limited effect on total PM2.5 concentrations and that the observed seasonal changes are likely the result of background influence, as opposed to modest changes in local contributions. Overall, the results demonstrate that in the Dallas–Fort Worth metropolitan area, approximately half of the observed total PM2.5 is from background PM2.5 sources and half is from local PM2.5 sources. Among the local PM2.5 source contributions in the Dallas–Fort Worth metropolitan area, our analysis shows that the vast majority is from non-point sources, such as from the transportation sector. While local point sources may have some incremental site-specific local contribution, such contributions are not clearly distinguishable in the data evaluated. We present this approach as a roadmap for disentangling PM2.5 concentrations at different spatial levels (i.e., the local, regional, or state level) and from various sectors (i.e., residential, industrial, transport, etc.). This roadmap can help decision-makers to optimize mitigatory, regulatory, and/or community efforts towards reducing total community PM2.5 exposure.

Dynamic Courier Capacity Acquisition in Rapid Delivery Systems: A Deep Q-Learning Approach

With the recent boom of the gig economy, urban delivery systems have experienced substantial demand growth. In such systems, orders are delivered to customers from local distribution points respecting a delivery time promise. An important example is a restaurant meal delivery system, where delivery times are expected to be minutes after an order is placed. The system serves orders by making use of couriers that continuously perform pickups and deliveries. Operating such a rapid delivery system is very challenging, primarily because of the high service expectations and the considerable uncertainty in both demand and delivery capacity. Delivery providers typically plan courier shifts for an operating period based on a demand forecast. However, because of the high demand volatility, it may at times during the operating period be necessary to adjust and dynamically add couriers. We study the problem of dynamically adding courier capacity in a rapid delivery system and propose a deep reinforcement-learning approach to obtain a policy that balances the cost of adding couriers and the cost-of-service quality degradation because of insufficient delivery capacity. Specifically, we seek to ensure that a high fraction of orders is delivered on time with a small number of courier hours. A computational study in the meal delivery space shows that a learned policy outperforms policies representing current practice and demonstrates the potential of deep learning for solving operational problems in highly stochastic logistic settings. History: This paper has been accepted for the Transportation Science Special Issue on Machine-Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This work was supported by Agencia Nacional de Investigación y Desarrollo [72180404]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0042 .

Metaheuristics-based maximum power point tracking for PV systems: a review

<span lang="EN-US">Over the years, numerous maximum power point tracking (MPPT) methods have been developed to extract the maximum available power from PV arrays. They are generally categorized as conventional or metaheuristic methods. The most employed conventional methods include perturb and observe (P&O), hill climbing (HC), and incremental conductance (INC), due to their simplicity and ease of implementation. However, under partial shading condition (PSC), none of them can effectively locate a global maximum power point (GMPP) out of many local maximum power points (LMPPs). This results in significant power loss during PSC, prompting the development of various metaheuristic-based MPPT methods to address the problem. This paper reviews 38 existing metaheuristic-based MPPTs and 27 metaheuristic methods that have not yet been applied to any MPPT operation up to date. Metaphorically, these methods are divided into four categories: (i) evolutionary-based, (ii) physics-based, (iii) swarm-based, and (iv) human-based. The different MPPTs are compared in terms of complexity, converter topology, and PSC tracking capability. This paper is intended to serve as a one-stop resource for any researcher, practitioner, or advanced student seeking to develop a new metaheuristic-based MPPT method.</span>

Modeling SARS coronavirus-2 omicron variant dynamic via novel fractional derivatives with immunization and memory trace effects

The objective of this paper is to recommend an adjusted Susceptible-Exposed-Infectious-Removed (SEIR) model that characterizes the temporal patterns of various individuals affected by the omicron variant in an epidemic. This model considers factors such as vaccination, asymptomatic cases, indoor and outdoor air, hospitalizations, and deaths, as well as the impact of immunization and memory trace. While many recent studies overlook the complexities of multiple strains, including their diverse transmission rates and reaction to vaccines, this study introduces a new fractional derivative model to examine the spread of the omicron variant of COVID-19 and the implementation of a vaccination campaign. A thorough theoretical analysis is conducted, and the critical factor (Rcrnz) is calculated using the model equations. It is demonstrated that when Rcrnz is less than 1, the disease-free state is globally asymptotically stable, meaning that the epidemic diminishes. Moreover, the stability of both global and local equilibrium points is examined. Numerical simulations are employed to demonstrate the alignment between the numerical findings and theoretical characteristics. The model is adjusted to experimental data that reflect the progression of the omicron variant of COVID-19 in Guangzhou, exhibiting a satisfactory performance in predicting the number of infected individuals, thereby suggesting its capability to accurately estimate asymptomatic cases. Furthermore, to emphasize the benefits of employing fractional differential equations, the paper provides examples related to memory trace and hereditary characteristics. Moreover, the examined models are expected to be applied and expanded upon in order to contribute to the formulation of policies for disease control during times of limited vaccine availability. To summarize, the model appears to be a sufficient tool for researching and managing infectious diseases. It is projected that as the Omicron variant's prevalence declines, there will be a reduced need for respiratory-focused precautions.

Dynamical localization and slow thermalization in a class of disorder-free periodically driven one-dimensional interacting systems

We study if the interplay between dynamical localization and interactions in periodically driven quantum systems can give rise to anomalous thermalization behavior. Specifically, we consider one-dimensional models with interacting spinless fermions with nearest-neighbor hopping and density-density interactions, and a periodically driven on-site potential with spatial periodicity m=2 and m=4. At a dynamical localization point, these models evade thermalization either due to the presence of an extensive number of conserved quantities (for weak interactions) or due to the kinetic constraints caused by drive-induced resonances (for strong interactions). Our models therefore illustrate interesting mechanisms for generating constrained dynamics in Floquet systems which are difficult to realize in an undriven system.

Machine learning enabled identification of sheet metal localization

The Forming Limit Curve (FLC), which describes the maximum applicable strain before localization, depends on the particular material, but also on the applied load and history of the load. Recent investigations have shown that the non-proportional loading effect on the FLC can be predicted with data-driven or machine-learning-based methods. Here we compare different ML methods to their applicability in predicting localization points under multi-segmented non-proportional loading. Therefore, an FE-based metamodel is developed that allows imposing an arbitrary loading history on sheet metal to predict the point of localization. A series of virtual experiments are conducted with this metamodel to generate a database of bi-linear loading paths that are used for training. Different ML-based methods were used to predict the localization point based on the strain history data. The 1D-Convolutional Neural Network (1D-CNN), with the ability to learn dependency between input features, has the best accuracy in predicting the localization point.

A novel damage detection method for carbon fibre reinforced polymer structures based on distributed strain measurements with fibre optical sensor

This paper presents a novel approach for damage detection in carbon fibre reinforced polymer (CFRP) structures based on local strain feature matching. The proposed approach utilizes distributed in-plane strain measurements for plate structures under load (in-service application) to define a local point autocorrelation coefficient based on Delaunay triangulation and Hausdorff distance. This coefficient effectively identifies the discontinuities in distributed in-plate strain, enabling accurate capture of damage information through the damage index which is defined as the integral of this coefficient. Notably, the proposed approach requires no prior knowledge of the non-damaged strain distribution and yields excellent detection results for both Barely Visible Impact Damage (BVID) and Visible Impact Damage (VID). Leveraging the advantages of distributed strain sensing, coupled with image processing technology for the purpose of in-service damage detection of CFRP structures, the proposed novel method is a promising technology. The efficacy of this technology has been demonstrated in this paper through theoretical analysis and experimental investigations, offering valuable insights and reference for future research in this domain.

Inorganic–organic hybrid quantum dots for AOP-mediated photodegradation of ofloxacin and para-nitrophenol in diverse water matrices

Due to concerns about the accessibility of clean water and the quality of treated wastewater, developing a suitable solution to enhance the water quality is critical. Thus, the current study focused on the synthesis of cadmium-doped CdIn2S4 incorporated in chitosan, forming Cd/CdIn2S4@Ch quantum dots using a solvothermal technique for the efficient photodegradation of hazardous pollutants like ofloxacin and para-nitrophenol through H2O2-mediated AOP. Cd/CdIn2S4@Ch quantum dots were characterized by several advanced methods, including XRD, PL, UV-DRS, FTIR, SEM, HR-TEM, XPS, DSC, TGA, EDX, and Elemental mapping analysis. The influence of varying reaction parameters, such as the effect of organic compounds, inorganic ions, and water matrices, was also investigated. The prepared composite showed outstanding photodegradation efficiency of 85.51 ± 1.35% and 96.70 ± 1.31%, with a rate constant of 0.02334 and 0.15134 min−1, which is about 1.24 and 2.07 times higher than pristine CdIn2S4 for ofloxacin and para-nitrophenol, respectively. The COD values were reduced to 80.67 ± 1.67% for ofloxacin and 88.36 ± 1.43% for para-nitrophenol, whereas the TOC values reduced to 73.49% and 86.34%, respectively, from their initial values. The improved performance is ascribed to the encapsulation of CdIn2S4 by chitosan, leading to the self-doping of cadmium into the photocatalyst, as the incorporated cadmium doping site can generate a local electron accumulation point, improving the charge separation efficacy and surface charge mitigation capability of chitosan nanosheets even further. The scavenger experiments showed that hydroxyl and superoxide radicals played a significant part in the photodegradation of contaminants. Additionally, the quantum dots showed excellent constancy and were recyclable up to six times, suggesting exceptional stability and reusability of the manufactured photocatalyst. The fabricated Cd/CdIn2S4@Ch quantum dots could be an excellent photocatalyst for removing organic pollutants from wastewater in the near future.

Tracking of horizontal alignment of the long and large diameter uncharged hole in NATM tunnel

The long and large diameter uncharged hole boring (LLB) method is a cut blasting method that minimizes blast-induced vibrations by creating long and large diameter uncharged holes at the excavation face of tunnels prior to tunnel excavation. Drilling in this method typically uses a 50 m long with a 382 mm diameter hammer bit in the horizontal direction at the tunnel face. However, the significant weight and uni-directional rotation of the rod head, as well as variables such as geological characteristics, machine conditions, and inexperienced operators result in significant deviation from the target borehole alignment that hinders the vibration-dampening effect of the uncharged holes. Furthermore, since there is no method to verify the alignment of the boreholes until main tunnel construction, borehole misalignment is often not discovered until weeks after construction, which requires tunnel construction to cease until the equipment can be remobilized and an additional borehole be created, causing significant delays and increased costs for the entire tunnel project. In this study, the borehole alignment tracking and ground exploration system (BGS) is developed to predict and monitor the quality and alignment of boreholes for cut blasting methods such as the LLB methods immediately after boring. The BGS was subsequently tested at a subway construction site to evaluate its performance in the field. The measurements yielded by the BGS were compared with manually measured boring positions at every 5 m along the borehole. Although the BGS showed a maximum deviation of approximately 12% at a local point where the hole surface was relatively rough, the accuracy for the final boring position was approximately 97%, demonstrating excellent precision of the alignment tracking system. The BGS demonstrates excellent performance in predicting ground conditions and the boring quality of a cut hole immediately after drilling, and shows promise in various other applications for monitoring borehole alignment.

Symmetry analysis, optimal system, and invariant solutions for a (2+1)-dimensional two-phase mass flow model

The paper investigates a two-phase mass flow model governed by gravity, involving solid particles and a viscous fluid. By utilizing the Lie symmetries admitted by the system, similarity solutions for the (2+1)-dimensional two-phase mass flow model are obtained. A comprehensive set of local point symmetries is established, and a well-suited collection of two-dimensional subalgebras is constructed from the maximal Lie invariance algebra. The optimal system’s vector fields are then utilized to directly reduce the governing model to a system of ordinary differential equations. Through analytical solutions, we successfully solve the resulting systems and further analyze their physical behaviors numerically.

A Profound Multitask System for Gender Identification face recognition, Confront Discovery, Point of interest Localization, and Head Position Estimation Hyperface

Machine learning is a technology that has risen in its usage and popularity in the last few years. A huge number of people from around the world are learning this technology and putting the knowledge to various use. Machine learning algorithms are capable of learning from the provided data with high accuracy. Even though a significant amount of research has been conducted on face recognition, the integrated model of face recognition, landmark localization, head posture estimation, and gender identification that is capable of high accuracy and speed has not yet been investigated. As a result, we have developed a face recognition system that can make predictions about photos that are comparable to those made by humans. The principal component analysis PCA and the SVM were used here to accomplish facial recognition. In feature extraction, to reduce the dimensionality of large datasets, principal component analysis is performed. After the data have been preprocessed, they are entered into the SVM classifier to be used for image classification. The study of this is done via visualization, and it is used to measure the effectiveness of the model. This face recognition algorithm has an accuracy of at least 80% when it comes to classifying people's portraits. The findings of the experiments show that the suggested technique can successfully identify faces since it employs a feature-based algorithm that combines PCA classification and SVM detection.

Nonlocal symmetries, nonlocally related systems, similarity solutions and conservation laws of the Tzitzéica-Dodd-Bullogh equation

In this paper, a methodical procedure is utilized for the identification of nonlocal symmetries of the (1+1)-dimensional Tzitzéica-Dodd-Bullogh equation. Firstly, by introducing a set of canonical coordinates corresponding to the local Lie point symmetries, the considered partial differential eq. (PDE) is mapped to an invertibly equivalent PDE system. Furthermore, nonlocal symmetries are obtained from the inverse potential system of the invertibly equivalent PDE system. The exact solutions for the aforementioned PDE are acquired with the help of the extended generalized Kudryashov method corresponding to the admitted symmetries. In addition, the derivation of local conservation laws for the Tzitzéica-Dodd-Bullogh equation is obtained through the multiplier method. Moreover, using a symmetry-based technique and local conservation principles, a complete tree of nonlocally related PDE systems has been constructed.