Combining depth and intensity images to produce enhanced object detection for use in a robotic colony

Robotic colonies that can communicate with each other and interact with their ambient environments can be utilized for a wide range of research and industrial applications. However amongst the problems that these colonies face is that of the isolating objects within an environment. Robotic colonies that can isolate objects within the environment can not only map that environment in de-tail, but interact with that ambient space. Many object recognition techniques ex-ist, however these are often complex and computationally expensive, leading to overly complex implementations. In this paper a simple model is proposed to isolate objects, these can then be recognize and tagged. The model will be using 2D and 3D perspectives of the perceptual data to produce a probability map of the outline of an object, therefore addressing the defects that exist with 2D and 3D image techniques. Some of the defects that will be addressed are; low level illumination and objects at similar depths. These issues may not be completely solved, however, the model provided will provide results confident enough for use in a robotic colony

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Video Deepfake Classification Using Particle Swarm Optimization-based Evolving Ensemble Models

The recent breakthrough of deep learning based generative models has led to the escalated generation of photo-realistic synthetic videos with significant visual quality. Automated reliable detection of such forged videos requires the extraction of fine-grained discriminative spatial-temporal cues. To tackle such challenges, we propose weighted and evolving ensemble models comprising 3D Convolutional Neural Networks (CNNs) and CNN-Recurrent Neural Networks (RNNs) with Particle Swarm Optimization (PSO) based network topology and hyper-parameter optimization for video authenticity classification. A new PSO algorithm is proposed, which embeds Muller’s method and fixed-point iteration based leader enhancement, reinforcement learning-based optimal search action selection, a petal spiral simulated search mechanism, and cross-breed elite signal generation based on adaptive geometric surfaces. The PSO variant optimizes the RNN topologies in CNN-RNN, as well as key learning configurations of 3D CNNs, with the attempt to extract effective discriminative spatial-temporal cues. Both weighted and evolving ensemble strategies are used for ensemble formulation with aforementioned optimized networks as base classifiers. In particular, the proposed PSO algorithm is used to identify optimal subsets of optimized base networks for dynamic ensemble generation to balance between ensemble complexity and performance. Evaluated using several well-known synthetic video datasets, our approach outperforms existing studies and various ensemble models devised by other search methods with statistical significance for video authenticity classification. The proposed PSO model also illustrates statistical superiority over a number of search methods for solving optimization problems pertaining to a variety of artificial landscapes with diverse geometrical layouts

Video deepfake detection using Particle Swarm Optimization improved deep neural networks

As complexity and capabilities of Artificial Intelligence technologies increase, so does its potential for misuse. Deepfake videos are an example. They are created with generative models which produce media that replicates the voices and faces of real people. Deepfake videos may be entertaining, but they may also put privacy and security at risk. A criminal may forge a video of a politician or another notable person in order to affect public opinions or deceive others. Approaches for detecting and protecting against these types of forgery must evolve as well as the methods of generation to ensure that proper information is supplied and to mitigate the risks associated with the fast evolution of deepfakes. This research exploits the effectiveness of deepfake detection algorithms with the application of a Particle Swarm Optimization (PSO) variant for hyperparameter selection. Since Convolutional Neural Networks excel in recognizing objects and patterns in visual data while Recurrent Neural Networks are proficient at handling sequential data, in this research, we propose a hybrid EfficientNet-Gated Recurrent Unit (GRU) network as well as EfficientNet-B0-based transfer learning for video forgery classification. A new PSO algorithm is proposed for hyperparameter search, which incorporates composite leaders and reinforcement learning-based search strategy allocation to mitigate premature convergence. To assess whether an image or a video is manipulated, both models are trained on datasets containing deepfake and genuine photographs and videos. The empirical results indicate that the proposed PSO-based EfficientNet-GRU and EfficientNet-B0 networks outperform the counterparts with manual and optimal learning configurations yielded by other search methods for several deepfake datasets

Bio-inspired multi-agent systems for reconfigurable manufacturing systems

The current market’s demand for customization and responsiveness is a major challenge for producing intelligent, adaptive manufacturing systems. The Multi-Agent System (MAS) paradigm offers an alternative way to design this kind of system based on decentralized control using distributed, autonomous agents, thus replacing the traditional centralized control approach. The MAS solutions provide modularity, flexibility and robustness, thus addressing the responsiveness property, but usually do not consider true adaptation and re-configuration. Understanding how, in nature, complex things are performed in a simple and effective way allows us to mimic nature’s insights and develop powerful adaptive systems that able to evolve, thus dealing with the current challenges imposed on manufactur- ing systems. The paper provides an overview of some of the principles found in nature and biology and analyses the effectiveness of bio-inspired methods, which are used to enhance multi-agent systems to solve complex engineering problems, especially in the manufacturing field. An industrial automation case study is used to illustrate a bio-inspired method based on potential fields to dynamically route pallets

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

IDRCNN: A Novel Deep Learning Network Model for Pancreatic Ductal Adenocarcinoma Detection on Computed Tomography

Early identification of pancreatic ductal adenocarcinoma (PDAC) improves prognosis. Still, it is difficult since lesions are generally smaller and difficult to define on contrast-enhanced computed tomography images (CE-CT). Ineffective PDAC diagnosis has recently been achieved using deep learning models, but the output localized and identified images are of poor quality. This research focuses on small lesions and presents a new, efficient automatic deep-learning network model for PDAC detection. The Improved Deep Residual Convolutional Neural Network (IDRCNN) detects PDAC. The hyperparameter is optimized using the Tunicate Swarm Optimization Algorithm (TSOA) algorithm. A better diagnosis is made due to segmenting the surrounding anatomy structure effects, such as PD. We train a proposed IDRCNN model for segmenting and detecting lesions automatically using CE-CT images. Two more IDRCNN models are trained with the aim of investigating the effects of anatomy integration: (i) segmentation of tumor and pancreas (IDRCNN_TP), and (ii) segmentation of pancreatic Duct (IDRCNN_PD). The three networks\u27 performance was assessed using an external, publicly available test set. Due to its effective classification results, the proposed method produces improved identification results for automated preliminary diagnosis of PDAC in cervical cancer clinics and hospitals. The performance of the proposed method is evaluated using a publicly assessable CT image dataset. It outperforms the existing state-of-the-art methods and achieved 98.67% accuracy, 97.26% recall, 98.52% precision, 97.65% sensitivity, and 98.45% specificity for pancreatic tumor detection