AI, Vol. 5, Pages 555-575: Development of an Attention Mechanism for Task-Adaptive Heterogeneous Robot Teaming AI doi: 10.3390/ai5020029 Authors: Yibei Guo Chao Huang Rui Liu The allure of team scale and functional diversity has led to the promising adoption of heterogeneous multi-robot systems (HMRS) in complex, large-scale operations such as disaster search and rescue, site surveillance, and social security. These systems, which coordinate multiple robots of varying functions and quantities, face the significant challenge of accurately assembling robot teams that meet the dynamic needs of ta ..read more

AI, Vol. 5, Pages 550-554: Artificial Intelligence in Healthcare: ChatGPT and Beyond AI doi: 10.3390/ai5020028 Authors: Tim Hulsen Artificial intelligence (AI), the simulation of human intelligence processes by machines, is having a growing impact on healthcare ..read more

AI, Vol. 5, Pages 533-549: ANNs Predicting Noisy Signals in Electronic Circuits: A Model Predicting the Signal Trend in Amplification Systems AI doi: 10.3390/ai5020027 Authors: Alessandro Massaro In the proposed paper, an artificial neural network (ANN) algorithm is applied to predict the electronic circuit outputs of voltage signals in Industry 4.0/5.0 scenarios. This approach is suitable to predict possible uncorrected behavior of control circuits affected by unknown noises, and to reproduce a testbed method simulating the noise effect influencing the amplification of an input sinusoidal vol ..read more

AI, Vol. 5, Pages 516-532: Fetal Hypoxia Detection Using Machine Learning: A Narrative Review AI doi: 10.3390/ai5020026 Authors: Nawaf Alharbi Mustafa Youldash Duha Alotaibi Haya Aldossary Reema Albrahim Reham Alzahrani Wahbia Ahmed Saleh Sunday O. Olatunji May Issa Aldossary Fetal hypoxia is a condition characterized by a lack of oxygen supply in a developing fetus in the womb. It can cause potential risks, leading to abnormalities, birth defects, and even mortality. Cardiotocograph (CTG) monitoring is among the techniques that can detect any signs of fetal distress, including hypoxia. Due to ..read more

AI, Vol. 5, Pages 504-515: Towards an ELSA Curriculum for Data Scientists AI doi: 10.3390/ai5020025 Authors: Maria Christoforaki Oya Deniz Beyan The use of artificial intelligence (AI) applications in a growing number of domains in recent years has put into focus the ethical, legal, and societal aspects (ELSA) of these technologies and the relevant challenges they pose. In this paper, we propose an ELSA curriculum for data scientists aiming to raise awareness about ELSA challenges in their work, provide them with a common language with the relevant domain experts in order to cooperate to find ..read more

AI, Vol. 5, Pages 482-503: ECARRNet: An Efficient LSTM-Based Ensembled Deep Neural Network Architecture for Railway Fault Detection AI doi: 10.3390/ai5020024 Authors: Salman Ibne Eunus Shahriar Hossain A. E. M. Ridwan Ashik Adnan Md. Saiful Islam Dewan Ziaul Karim Golam Rabiul Alam Jia Uddin Accidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual detection of such faults periodically can be both ti ..read more

AI, Vol. 5, Pages 465-481: Visual Analytics in Explaining Neural Networks with Neuron Clustering AI doi: 10.3390/ai5020023 Authors: Gulsum Alicioglu Bo Sun Deep learning (DL) models have achieved state-of-the-art performance in many domains. The interpretation of their working mechanisms and decision-making process is essential because of their complex structure and black-box nature, especially for sensitive domains such as healthcare. Visual analytics (VA) combined with DL methods have been widely used to discover data insights, but they often encounter visual clutter (VC) issues. This study ..read more

AI, Vol. 5, Pages 426-445: Single Image Super Resolution Using Deep Residual Learning AI doi: 10.3390/ai5010021 Authors: Moiz Hassan Kandasamy Illanko Xavier N. Fernando Single Image Super Resolution (SSIR) is an intriguing research topic in computer vision where the goal is to create high-resolution images from low-resolution ones using innovative techniques. SSIR has numerous applications in fields such as medical/satellite imaging, remote target identification and autonomous vehicles. Compared to interpolation based traditional approaches, deep learning techniques have recently gained atten ..read more

AI, Vol. 5, Pages 446-464: Trust-Aware Reflective Control for Fault-Resilient Dynamic Task Response in Human–Swarm Cooperation AI doi: 10.3390/ai5010022 Authors: Yibei Guo Yijiang Pang Joseph Lyons Michael Lewis Katia Sycara Rui Liu Due to the complexity of real-world deployments, a robot swarm is required to dynamically respond to tasks such as tracking multiple vehicles and continuously searching for victims. Frequent task assignments eliminate the need for system calibration time, but they also introduce uncertainty from previous tasks, which can undermine swarm performance. There ..read more

AI, Vol. 5, Pages 405-425: Few-Shot Fine-Grained Image Classification: A Comprehensive Review AI doi: 10.3390/ai5010020 Authors: Jie Ren Changmiao Li Yaohui An Weichuan Zhang Changming Sun Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of images (e.g., birds, flowers, and airplanes) belonging to different subclasses of the same species by a small number of labeled samples. Through feature representation learning, FSFGIC methods can make better use of limited sample information, learn more discriminative feature representations, greatly improve the class ..read more