Path Finder Research Articles

Optimal object stitching and video condensation-aided improved YOLOv5-oriented anomaly object detection framework

Recent advances in intelligent surveillance to boost public security have increased interest in anomaly location and identification among computer vision researchers in congested situations. Unrestricted video monitoring is required due to its extensive use in private and public spheres. Particularly, research on finding anomalies in surveillance videos has drawn much interest. Video condensation is an efficient method for quickly scanning and retrieving lengthy surveillance films. Reordering the video events in the spatial or temporal domain seeks to condense large video sequences into their equivalent compact video representation. Additionally, if a delay occurs in anomaly detection, it leads to time consumption for rectifying it. Therefore, the core aim of this task is to reduce the time consumption of detecting the instance of video anomaly detection faster by introducing the optimal object stitching through the deep learning-based video condensation concept. The steps include video condensation and deep learning-based anomaly detection. Initially, the necessary video for anomaly detection is gathered from the datasets. The gathered video is converted into frames and is used for object detection in each frame using Improved YOLOv5 (I-YOLOv5). Then, the optimal frame selection is performed using the hybrid Fitness of Wild Geese and Path Finder (FWGPF) for stitching the detected objects into the selected optimal frames, thus compressing the relevant frames. In the second phase, the condensed (compressed) frames are utilized for determining the anomaly frames with the support of Parameter Tuning in Dilated MobileNet (PTDM), and further, the anomaly objects are detected on the classified condensed frames based on the I-YOLOv5. Here, the tuning of constraint is done in the anomaly detection phase by the same hybrid FWGPF to reduce the time consumption. The experimental analysis of the developed model is performed with baseline algorithms and detection techniques to confirm the developed model’s efficiency.

Design of Novel Brain Tumor Segmentation System Using Hybrid Heuristic-Aided Multiscale Self-Guided Attention Mechanism-Based Adaptive Unet+++ with 3D Brain MRI Images

Segmentation of brain tumors attains great importance in the medical industry. As the brain tumor causes an earlier death, detection and diagnosis are required. Generally, brain tumor diagnosis is done by considering Magnetic Resonance Imaging (MRI) because it has superficial feature information. In the existing works, the different segmentation process is analyzed, yet it is more time-consuming, and has complexities. Thus, building of an efficient segmentation model is a quite challenging task. As the former implemented models are not in place to segment the abnormal region properly, it suggests developing an effective automated model using recently emerged techniques of deep learning. To surmount such challenging factors, a novel 3D brain tumor segmentation model is proposed with hybrid heuristic development. Initially, the brain images are collected from the standard benchmark datasets. The collected brain images are pre-processed using the adaptive technique of Contrast Limited Adaptive Histogram Equalization (CLAHE). The pre-processed images are segmented with the developed Multiscale Self-Guided Attention Mechanism-based Adaptive UNet3[Formula: see text] (MSGAM-AUNet3[Formula: see text]), where the parameters are optimized with the hybrid optimization strategy of Modified Path Finder Coyote Optimization (MPFCO) to elevate the segmentation performance. The experimental analysis is carried out to estimate the efficiency of the developed framework with the comparison using diverse segmentation techniques.

Design and modelling of an AI governed type-2 Fuzzy tilt control strategy for AGC of a multi-source power grid in constraint to optimal dispatch

The robust design and application of a novel type-2 fuzzy tilt controller (Type2-FTC) has been well demonstrated in this manuscript. This controller is anticipated to build automatic generation control (AGC) of a multi-area hybrid power system in concern to the optimal power generation scheduling. The combined action of AGC and optimal scheduling of a power system is referred as economic AGC or commonly ELD-AGC. At the initial stage of the analysis, the concept of only AGC has been discussed through various robust approaches. Then the extensive study on ELD-AGC of the proposed power system has been progressed under various controller actions. Further, the proposed Type2-FTC controller is designed ideally with employing an innovative quassi-opposition path finder algorithm (QO-PFA) in various operating conditions. The effectiveness and the viability of the proposed Type2-FTC controller has been verified over type-I fuzzy PID and conventional PID controllers for AGC as well as ELD-AGC of the multi area power system. In controller comparison study, it has been revealed that proposed Type2-FTC is a potential candidate to improve frequency stability of the system. Further, the analysis over technique study justifies supremacy of the suggested QO-FPA algorithm over standard PFA and PSO algorithms for optimal designing all implemented controllers.

An Enhanced Path Finder Algorithm for the estimation of the stator current envelope to detect rotor bar breakage in an induction motor

The Enhanced Path Finder Algorithm (EPFA) is used in this research to present an innovative approach for examining the state of the rotor bars in an induction motor, employing a hybrid metaheuristic algorithm. Compared to current optimization techniques, the suggested approach permits more balanced exploration and exploitation. The method addresses the drawbacks of traditional analysis of fault related frequencies, such as short data length, spectrum leakages, and inadequate sampling frequency. The primary goal of this research is to identify the twice-slip frequency component and its harmonics in the current envelope that correlates with the fault. The A signal model of the stator current envelope and the residual error function have been used to define a nonlinear least-squares problem. An induction machine model has been developed in 2D ANSYS Maxwell software where faults have been implemented and studied. Finally, the efficiency of the fault detection scheme has been verified with a practical data set.

Flavor profiling and gene expression studies of indigenous aromatic rice variety (Mushk Budiji) grown at different altitudes of Highland Himalayan regions

Mushk Budiji-an indigenous aromatic rice variety is usually grown at an altitude ranging from 5000 to 7000 ft above mean sea level in Highland Himalayas. This study was conducted to investigate the effects of altitude, soil nitrogen content and climatic conditions (temperature) of the selected locations on the flavor profile of Mushk Budiji using gas chromatography-mass spectroscopy (GC–MS) and electronic nose (E-nose). E-nose being rapid and non-destructive method was used to validate the results of volatile aromatic compounds obtained using GC–MS in Mushk Budiji. Around 35 aromatic compounds were identified in Mushk Budiji rice samples. Highest volatile peak area percentage (105.41%) was recorded for Mushk Budji grown at an altitude of 5216.53 ft. Highest E-nose score (2.52) was obtained at an altitude of 6299.21 ft. Over-expression of fatty acid degradation and linoleic acid metabolism genes was observed at higher altitudes, whereas lipid biosynthesis was negatively influenced by higher altitude. Fatty acid degradation and linoleic acid metabolism is responsible for the synthesis of volatile aromatic compounds in Mushk Budiji. This study will therefore be the path finder for investigating the intricate mechanism behind the role of altitude on aroma development in Mushk Budiji rice for future studies.

DiabPrednet: development of attention-based long short-term memory-based diabetes prediction model with optimal weighted feature fusion mechanism

ABSTRACT Machine learning is a computer technique that automatically learns from experience and enhances the effectiveness of producing more precise diabetes predictions. However, large, inclusive, high-quality datasets are needed for training the machine learning networks. In this research work, attention-based approaches are designed for predicting diabetes in the affected individuals. Initially, the collected diabetes data is given into the data cleaning to get noise-free data for the prediction task. Here, extracted feature set 1 is extracted from the Auto encoder, and extracted feature set 2 is extracted from the 1-Dimensional Convolutional Neural Network (1D-CNN). These two sets of extracted features are fused in the adaptive way that is weighted feature fusion. Here, the weight of the selected features is optimized by an Enhanced Path Finder Algorithm (EPFA) to get more accurate results. The weighted fused features are employed for the diabetes prediction phase, in which the developed Attention-based Long Short Term Memory (ALSTM) with architecture optimization by improved PFA for predicting diabetes in affected one. Throughout the result analysis, the designed method attains 95% accuracy and 92%precision rate. Finally, the analysis is made by the proposed and existing prediction methods to showcase the effective performance.

IBN ARABI'S CREATIVE IMAGINATION IN ODHY POETRY OF SUFISM FIGURES IN HIS ANTHOLOGY RAHASIA SANG GURU SUFI

This research aims to analyze the Sufism figure in Odhy poetry anthology Rahasia Sang Guru Sufi through Ibn Arabi's creative imagination. The Sufism figure has an important role in the Sufism discourse, and creative imagination is related to literary discourse. Ibn Arabi’s creative imagination combined indicated Ibn Arabi himself and his imagination. Ibn Arabi’s thought is compatible with the literature and applied as a theoretical approach to mystical literature, namely the work of Odhy, the Sufism figure poetry. Those terms denote part and relate to Sufism studies, especially a murshid (guru) to guide the spiritual guidance through stations until the peak. Those path finders indicate the creative imagination that Odhy used in his poetry collection; at the same time, the literature has related to that imagination. This research reveals the mutual connection between literature and Islamic mysticism based on imagination. This figure is a person who wrote down his role in the discourse and history of Sufism. The data on Sufism figures are collected through the descriptive method from the primary source of those anthologies. Besides, the historical approach explains his poetries in those anthologies. This research found the Sufism figures as a thought in Islamic Mysticism in history through Ibn Arabi’s creative imagination and an image resource in literature.

Playtesting: What is Beyond Personas

We present two approaches to improve automated playtesting. First, we propose developing persona, which allows a persona to progress to different goals. In contrast, the procedural persona is fixed to a single goal. Second, a human playtester knows which paths she has tested before, and during the consequent tests, she may test different paths. However, Reinforcement Learning (RL) agents disregard these previous paths. We propose a novel methodology that we refer to as Alternative Path Finder (APF). We train APF with previous paths and employ APF during the training of a RL agent. APF modulates the reward structure of the environment while preserving the agent's goal. When evaluated, the agent generates a different trajectory that achieves the same goal. We use the General Video Game Artificial Intelligence and VizDoom frameworks to test our proposed methodologies. We use Proximal Policy Optimization RL agent during experiments. First, we compare the playtest data generated by developing and procedural persona. Our experiments show that developing persona provides better insight into the game and how different players would play. Second, we present the alternative paths found using APF and argue why traditional RL agents cannot learn those paths.

Novel QO-PFA governed FO-type-II fuzzy controller for LFC of thermo-electric generator based hybrid power system

The present article addresses various novel and advanced approaches to mitigate frequency oscillation issues in an integrated power system. The suggested model of the multi-source power system is basically a two-area power system, where area2 is completely a microgrid system. The diesel generator, thermo-electric generator, wind generator, solar PV system, micro-turbine and fuel cell are the basic power generating sources in the microgrid system. The microgrid also has been associated with various energy saving equipments like battery and flywheel energy storage (FES) system to improve the power quality of the system. The unpredictable wind speed variant and fluctuation in solar power incorporates oscillations in voltage and frequency profile of the interconnected power system. In challenging to above issues, this research article has designed an innovative Fractional Order-type-2 fuzzy controller (FO-T2FC) to supress unwanted errors in the frequency and voltage of the system. Further, the proposed controller parameters are selected optimally with suggesting a novel Quassi- Oppositional Path Finder Algorithm (QO-PFA) under different operatings. An analysis on relative study is exercised to validate strength of the FO-T2FC control strategy and QO-PFA algorithm. Owing to the performance analysis, it is justified that proposed QO-PFA designed FO-T2FC controller is proved to be most potential candidate and able to improve the dynamic frequency response of area1 (Δf1) by 251.74% and 418.74% as compared to Type1 fuzzy controller and conventional PID controller respectively.

BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization.

Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature.

Path Finder Optimization Algorithm Tuned 3DOFPID Controller for Frequency Stabilization in Wind Integrated Realistic Power System with HVDC line

In this paper, a novel controller is suggested for the stabilization of the frequency of wind integrated realistic power system (WIRPS) during load uncertainties. 3-degree-of-freedom PID (3DOFPID) based on the pathfinder optimization algorithm (PFOA) is proposed in this paper. Initially, the performance of PFOA tuned 3DOFPID is tested on a widely implemented model of dual area hydro-thermal power system (DAHTPS) for 10% step load perturbation in area-1. Later, the suggested controller performance is analyzed on WIRPS for 10%SLP on area-1. Investigation of WIRPS is carried out by considering and omitting the non-linearity constraint of communication time delays (CTDs). The efficacy of the presented controller efficacy is showcased with the other control techniques. Moreover, the impact of CTDs on WIRPS is demonstrated and a further high voltage DC (HVDC) line is laid with the WIRPS for performance enhancement. The robustness of the presented strategy is established finally with the robustness test.

A Multi Objective Hybrid Collision-free Optimal Path Finder for Autonomous Robots in Known Static Environments

The most important field of robotics study is path planning. Path planning problem in general is an NP-complete problem. Though several attempts have been made using A*, PRM, RRT, and RRT* these algorithms explore too many nodes in the state space, not completely captured kinematic constraints, and are not optimal in real-time. In this paper, a Multi-Objective Hybrid Collision- free Optimal Path Finder (MOHC-OPF) is proposed which is an attempt to obtain a near-optimal solution by exploring fewer nodes compare to the above existing methods while considering kinematic constraints aiming to generate optimal drivable paths. The empirical study revealed that the proposed algorithm is capable of detecting static obstacles and finding a collision-free nearest-optimal, smooth and safe path to the destination in a static known environment. Multiple criteria, including path length, collision-free, execution time, and smooth path, are used to determine an optimal path.. The proposed algorithm shows efficiency in finding the shortest path length and execution time decreased in 90% of the experiments.

An Optimal Parameterized Fractional-Order PID Controller for the Single Phase Grid Integrated with Solar and Wind System

The hybrid solar/wind power systems are the best renewable energy sources because of their complementary nature. The generation of electricity from wind and solar photovoltaic (PV) systems is greatly reliant on the weather. Their intermittent nature causes output fluctuations. As a result, the need for rapid compensation for energy transmission and distribution systems is becoming increasingly critical. In this article, an optimal parameterized Fractional Order Proportional Integral Derivative (FOPID) controller for a hybrid renewable energy system integrated with a single-phase grid. An amalgamation of solar PV and wind is presented as a way to provide continuous power from alternate sources. Furthermore, it is planned to design a robust output voltage management system that utilizes a FOPID controller for the output voltage regulation of the hybrid system integrated with the grid. A novel Randomly Updated Path Finder Algorithm (RU-PF) is proposed to optimize the FOPID gains. Hence, the proposed FOPID-based control for the single-phase grid connected to PV and wind system is simulated in MATLAB/Simulink, and their performance is examined. In particular, the fitness of the proposed RU-PF algorithm is 5.06%, 1.32%, 7.41%, and 0.88% superior to the extant PSO, FF, WOA, and PFA algorithms at the 25th iteration.

Optimal Placement of UPQC in Distribution Network Using Hybrid Approach

This study represents a power quality enhancement approach using a new hybrid algorithm which merges both Path Finder Algorithm (PFA) and Grey Wolf Optimizer (GWO) for determining the precise position and sizing of a unified power quality conditioner (UPQC). For this optimal selection, the adopted model named as Grey Wolf Insisted Inertia-based Path finder Algorithm is used. To determine the best location of the UPQC device, the suggested model focuses on the cost of UPQC, power losses, and voltage stability index. Further, the presented concept was implemented using IEEE 69 and IEEE 33 bus networks and the proposed model’s performance was compared to that of other traditional approaches with respect to minimum fitness value. Accordingly, for a 50% loading scenario, the proposed model is 0.30%, 0.20%, 0.348%, 0.277%, and 0.105% better than PF, GWO, GM-DA, DA, and GA schemes. Likewise, in convergence analysis for 100th iteration, the suggested approach reaches the least value of 536.80. Therefore, it is evident that the proposed PF-GWO algorithm is more efficient than existing models with lower convergence rates. Thus, it is concluded that the proposed model achieves power quality enhancement for the optimal location and sizing of UPQC in power systems.

Smart building evacuation system with hybrid network based CNC-C architecture

The main aim of this work is to implement a progressive path-planning algorithm with a proposed hybrid network based CNC-C (Cooperative Network Coded–Communication) architecture for a smart evacuation system. An algorithm ALCDTS (Health based Age–Length–Capacity–Distance–Trustiness–Speed) is proposed to generate possible progressive routes by considering the building conditions, hazard estimation, path capacity along with the occupant’s age, speed and health status. The investigation of ALCDTS is carried out by using the path finder simulator. The comparative study between the conventional evacuation algorithm and ALCDTS algorithm has been performed and the results are obtained. A four-storied shopping mall is taken as a building model where the performance of test cases for abnormal event is analysed.

Evaluation of the Canal Transportation Following Glide Path Preparation with Different Rotary Systems: A Comparative Study.

Objectives Ideal root canal shaping might be more challenging particularly in cases with severely curved canals or complex anatomical variations. Glide path preparation is suggested as a critical step to achieve ideal canal preparation. The present study is aimed at evaluating transportation at different levels of the canal following glide path preparation by five different path finders. Materials and Methods The study was conducted on 100 S-shaped canal simulator blocks. Glide path was prepared in five groups including (1) Scout RaCe (#10 and #15, 0.02), (2) One G (#14, 0.03), (3) PathFile (#13 and #16, 0.02), (4) GPS (#15, 0.03), and (5) K file (#15, 0.02) (Control group). The first four groups were NiTi rotary instruments, while the last group was a stainless steel hand file. The aforementioned files were used after canal negotiation by a #10 stainless steel hand file. Before- and after-preparation photos were taken and were superimposed in Adobe PhotoShop CC 2019. Transportation measurements were conducted in Digimizer. Absolute canal transportation was calculated at 10 cross-sections. Intergroup and intragroup data analysis were conducted using one-way and repeated measures ANOVA tests, respectively, in SPSS 26.0. The significance level was set to 0.05. Results Although K file led to significantly more transportation in the apical and middle thirds (p < 0.001), rotary groups were not statistically different. In the coronal third, K files led to significantly more transportation compared to Scout RaCe and PathFile (p < 0.05). Conclusion Within the limitations of the present study, regardless of the recruited rotary system, glide path preparation using NiTi rotary instruments leads to less canal transportation compared to stainless steel hand files.

Test Case Generation using Symbolic Execution

Testing is a well-known technique for identifying errors in software programs. Testing can be done in two ways: Static analysis and Dynamic analysis. Symbolic execution plays a vital role in static analysis for test case generation and to find the unreachable path with minimum test cases. Unreachable path is a part of a program which can never be executed i.e., the symbolic execution doesn’t continue for that path and the current execution stops there. It generates a test suite for loop-free programs that is achieved by path coverage. In the best case program loops implies increase in the number of paths exponentially and in the worst case the program will not terminate. The functions of symbolic execution are test input generation, unreachable path detection, finding bugs in software programs, debugging. In this paper, we focus on dead code detection and test input generation using symbolic execution. Our execution for Java programs uses Java Path Finder (JPF) model tester. Our analysis shows that the symbolic execution method can be used to reduce symbolic execution time and to find out the unreachable path with less number of test cases.

Multi-Objective Optimal Path Planning for Autonomous Robots with Moving Obstacles Avoidance in Dynamic Environments

Path planning is vital for robust autonomous robot navigation. Driving in dynamic environments is particularly difficult. The majority of the work is based on the premise that a robot possesses a comprehensive and precise representation of its surroundings prior to its starting. The problem of partially knowing and dynamic environments has received little attention. This circumstance occurs when an exploratory robot or a robot without a floor plan or terrain map must move to its destination. Existing approaches for dynamic-path-planning design a preliminary path based-on known knowledge of the environment, then adjust locally by replanning the total path as obstacles are discovered by the robot's sensors, thereby sacrificing either optimality or computational efficacy. This paper presents a novel algorithm. A Near-Optimal Multi-Objective Path Planner (NO-MOPP), capable of planning time-efficient, near-optimal, and drivable paths in partially known and dynamic environments. It is an expansion of our earlier research contributions called "A Multi-Objective Hybrid Collision-free Optimal Path Finder (MOHC-OPF) for Autonomous Robots in known static environments" and "A Multi-Objective Hybrid Collision-free Near-Optimal Path Planner (MOHC-NODPP) for Autonomous Robots in Dynamic environments". In the environment, a mix of static and moving dynamic obstacles are present, both of which are expressed by a hybrid, discrete configuration space in an occupancy-grid map. The proposed approach is executed at two distinct levels. Using our earlier method, A Multi-Objective Collision-free Optimal Path Finder (MOHC-OPF), the initial optimal path is found in environment that includes only known stationery obstacles at the Global-path-planning level. On the second level, known as Local Re-planning, this optimal path is continuously refined by online re-planning to account for the movement of obstacles in the environment. The proposed method, A Near-Optimal Multi-Objective Path Planner (NO-MOPP), is used to keep the robot's sub-paths optimum while also avoiding dynamic obstacles. This is done while still obeying the robot's non-holonomic restrictions. The proposed technique is tested in simulation using a collection of standard maps. The simulation findings demonstrate the proposed method's ability to avoid static as well as dynamic obstacles, as well as its capacity to find a near-optimal-path to a goal location in environments that are constantly changing without collision. The optimal-path is determined by taking into account several performance measures, including path length, collision-free path, execution time, and smooth paths. 90% of studies utilizing the proposed method demonstrate that it is more effective than other methods for determining the shortest length and time-efficient smooth drivable paths. The proposed technique reduced average 15% path length and execution time compared to the existing methods.

Intelligent Path Planning Approach for Autonomous Mobile Robot

This paper presents a new path planning algorithm for an autonomous mobile robot. It is desired that the robot reaches its goal in a known or partially known environment (e.g., a warehouse or an urban environment) and avoids collisions with walls and other obstacles. To this end, a new, efficient, simple, and flexible path finder strategy for the robot is proposed in this paper. With the proposed strategy, the optimal path from the robot’s current position to the goal position is guaranteed. The environment is represented as a grid-based map, which is then divided into a predefined number of subfields to reduce the number of required computations. This leads to a reduction in the load on the controller and allows a real-time response. To evaluate the flexibility and efficiency of the proposed strategy, several tests were simulated with environments of different sizes and obstacle distributions. The experimental results demonstrate the reliability and efficiency of the proposed algorithm.

Remote sensing, SWIR, Raman and XRD applications in Pesovets epithermal system mapping, Panagyurishte ore district, Bulgaria

Remote sensing UAV based study combined with field mapping, SWIR, XRD Raman and XRF tests for mineral detection outlined advanced argillic alteration domains in the Pesovets silica cap to demonstrate quick approach for epithermal gold exploration targeting and evaluation. As and Ti increasing trend toward epithermal high-sulphidation Cu-Au mineralization could be employed as a proximal path finder.