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The progress in technology has led to an increase in the amount of data that can be generated and processed. Downsampling methods are employed to eliminate unnecessary data but can also lead to the loss of valuable information. Herein, we designed a pooling algorithm, Conditional-Pooling , that provides a transitive structure composed of average (avg.) and max-pooling methods, and it hosts the advantageous behaviors from both. We examined our approach on several image recognition and detection tasks using deep neural networks. The results reveal that our method excels over major rivals like max-pooling and avg. pooling when tested on MNIST, Fashion-MNIST, and CIFAR for classification, Pascal VOC for detection, COCO minitrains, and ADORESet for both classification and detection. The output of Conditional-Pooling preserves important image features such as edges and corners and contains more salient features that lead to more accurate results. The code for Conditional-Pooling will be available at https://github.com/bayraktare/conditional_pooling. • Adaptive pooling for novel precision and accuracy especially for complex scenes. • Exceeding existing methods on popular visual tasks regarding various metrics. • Game-changing results for noise elimination and image downsampling. [ABSTRACT FROM AUTHOR]

In digital era, being stranded from very basic telecommunication protocols and internet makes vehicle rerouting-like crucial tools more difficult or even impossible, especially in times of disaster and emergency. In this study, we propose a modular rerouting framework for only one single vehicle composed of visual perception, property estimation and trajectory optimization, which enables to generate optimum paths exploiting aerial imagery. Once the deep network, which is fine-tuned on newly introduced dataset herein named MaVefAI, processes the input, the following module estimates pose, motion direction and speed of detected vehicles. Afterwards, we link the appropriate vehicles via graphs to obtain group properties that pave the way for estimating the traffic congestion level. In the end, we get the output as the optimum path from the independent trajectory optimization module to which required inputs are already sent by preceding modules. We solve the multi-objective cost function subject to velocity and congestion intervals, which comprises distance, traffic congestion level, and angle inconsistency. We employ Dijkstra, A*, RRT, and RRT* to optimize the cost while vast majority of existing works focus to optimize via single method. The fine-tuned segmentation model accuracy becomes more than 98% for vehicle groups thanks to MaVefAI. The extensive experiments reveal that all algorithms follow the same path. However, RRT* achieves the fastest result by examining most of the possible options in less time, which also appears to be the most robust method comparing with the alternatives for route optimization. Our dataset MaVefAI is publicly available here: https://precisioncomputing.sakarya.edu.tr. [ABSTRACT FROM AUTHOR]

Optimal path planning is a fundamental problem in artificial intelligence (AI) and has wide applications in areas such as robotics, transportation, and logistics. In this paper, we propose a novel approach for ordering algorithms in pathfinding problems using the concept of shell layers. Our approach aims to improve the computational efficiency and distance cost of ordering algorithms. To evaluate the effectiveness of our approach, we compare it with five state-of-the-art techniques commonly used in the field of ordering algorithms on eight different scenarios with varying configurations. Our results show that our proposed approach outperformed the state-of-the-art techniques in terms of time-computational complexity and distance cost in most of the scenarios, demonstrating its potential as a new state-of-the-art technique for ordering algorithms. Specifically, our approach outperformed the nearest neighbor, A*, branch and bound, Christofides, and genetics ordering algorithms. However, we also identified two specific situations where our approach was outperformed by two state-of-the-art algorithms due to the specific distribution of the goal nodes. These findings highlight the importance of evaluating and comparing ordering algorithms in various scenarios. Our approach has significant implications for AI research and development, as it has the potential to improve the performance of pathfinding algorithms in various applications. We also discuss the limitations of our approach and potential areas for further research, such as investigating the effectiveness of our approach in different types of graphs and exploring the potential use of machine learning techniques to optimize the shell layer construction. The code for this study is available at https://github.com/abdullah1aloush1/MuGONA. • Equally-spaced nodes formed over workspace with obstacles, optimizing search process. • Efficient neighboring graph built for equally-spaced nodes, reducing complexity. • Path optimization for navigation by ordering goal nodes, reducing time and distance. • Path smoothing for navigation via interpolation to yield a trajectory on path nodes. [ABSTRACT FROM AUTHOR]