Background: Predicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.Results: A highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function.Conclusion: We present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose newPNPRDfeatures representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.

Internet of Things (IoT) technology has been attracted lots of interests over the recent years, due to its applicability across the various domains. In particular, an IoT-based robot with artificial intelligence may be utilized in various fields of surveillance. In this paper, we propose an IoT platform with an intelligent surveillance robot using machine learning in order to overcome the limitations of the existing closed-circuit television (CCTV) which is installed fixed type. The IoT platform with a surveillance robot provides the smart monitoring as a role of active CCTV. The intelligent surveillance robot, which has been built with its own IoT server, and can carry out line tracing and acquire contextual information through the sensors to detect abnormal status in an environment. In addition, photos taken by its camera can be compared with stored images of normal state. If an abnormal status is detected, the manager receives an alarm via a smart phone. For user convenience, the client is provided with an app to control the robot remotely. In the case of image context processing it is useful to apply convolutional neural network (CNN)-based machine learning (ML), which is introduced for the precise detection and recognition of images or patterns, and from which can be expected a high performance of recognition. We designed the CNN model to support contextually-aware services of the IoT platform and to perform experiments for learning accuracy of the designed CNN model using dataset of images acquired from the robot. Experimental results showed that the accuracy of learning is over 0.98, which means that we achieved enhanced learning in image context recognition. The contribution of this paper is not only to implement an IoT platform with active CCTV robot but also to construct a CNN model for image-and-context-aware learning and intelligence enhancement of the proposed IoT platform. The proposed IoT platform, with an intelligent surveillance robot using machine learning, can be used to detect abnormal status in various industrial fields such as factory, smart farms, logistics warehouses, and public places.

The manufacturing industry environment is rapidly evolving into smart manufacturing. It prioritizes digital innovations such as AI and digital transformation (DX) to increase productivity and create value through automation and intelligence. Vision systems for defect detection and quality control are being implemented across industries, including electronics, semiconductors, printing, metal, food, and packaging. Small and medium-sized manufacturing companies are increasingly demanding smart factory solutions for quality control to create added value and enhance competitiveness. In this paper, we design and develop a high-speed defect detection system based on a line-scan camera using deep learning. The camera is positioned for side-view imaging, allowing for detailed inspection of the component mounting and soldering quality on PCBs. To detect defects on PCBs, the system gathers extensive images of both flawless and defective products to train a deep learning model. An AI engine generated through this deep learning process is then applied to conduct defect inspections. The developed high-speed defect detection system was evaluated to have an accuracy of 99.5% in the experiment. This will be highly beneficial for precision quality management in small- and medium-sized enterprises

Smart farms refer to spaces and technologies that utilize networks and automation to monitor and manage the environment and livestock without the constraints of time and space. As various devices installed on farms are connected to a network and automated, farm conditions can be observed remotely anytime and anywhere via smartphones or computers. These smart farms have evolved into smart livestock farming, which involves collecting, analyzing, and sharing data across the entire process from livestock production and growth to post-shipment distribution and consumption. This data-driven approach aids decision-making and creates new value. However, in the process of evolving smart farm technology into smart livestock farming, challenges remain in the essential requirements of data collection and intelligence. Many livestock farms face difficulties in applying intelligent technologies. In this paper, we propose an intelligent monitoring system framework for smart livestock farms using artificial intelligence technology and implement deep learning-based intelligent monitoring. To detect cattle lesions and inactive individuals within the barn, we apply the RT-DETR method instead of the traditional YOLO model. YOLOv5 and YOLOv8 are representative models in the YOLO series, both of which utilize Non-Maximum Suppression (NMS). NMS is a postprocessing technique used to eliminate redundant bounding boxes by calculating the Intersection over Union (IoU) between all predicted boxes. However, this process can be computationally intensive and may negatively impact both speed and accuracy in object detection tasks. In contrast, RT-DETR (Real-Time Detection Transformer) is a Transformer-based real-time object detection model that does not require NMS and achieves higher accuracy compared to the YOLO models. Given environments where large-scale datasets can be obtained via CCTV, Transformer-based detection methods like RT-DETR are expected to outperform traditional YOLO approaches in terms of detection performance. This approach reduces computational costs and optimizes query initialization, making it more suitable for the real-time detection of cattle maintenance behaviors and related abnormal behavior detection. Comparative analysis with the existing YOLO technique verifies RT-DETR and confirms that RT-DETR shows higher performance than YOLOv8. This research contributes to resolving the low accuracy and high redundancy of traditional YOLO models in behavior recognition, increasing the efficiency of livestock management, and improving productivity by applying deep learning to the smart monitoring of livestock farms.

Named data networking (NDN) is a content-centric network for the future of the internet. An NDN a packet is delivered based on a content name instead of a destination IP address. The name lookup for packet forwarding is challenging because the content name is variable, and its space is unbounded. This paper proposes a novel name lookup scheme that employs a hashing technique combined with Patricia tries. In this scheme, hash tables are dynamically maintained according to the hierarchical structure of the name, so the name can effectively accommodate variable and unbounded content names. Unlike chaining, a Patricia trie compares a key string only once at a leaf node, so it provides a fast name lookup. The proposed lookup scheme is implemented and evaluated by using an Intel Core i7-2600 CPU and 4GB of memory. Experimental results show that our scheme gives good scalability and a high throughput of name lookup with a controlled memory consumption.