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Edge computing, artificial intelligence (AI), and machine learning (ML) concepts have become increasingly prevalent in Internet of Things (IoT) applications. As the number of IoT devices continues to grow, relying solely on cloud computing for real-time data processing and analysis is proving to be more challenging. The synergy between edge computing and AI is particularly intriguing due to AI's reliance on rapid data processing, a capability facilitated by edge computing. Edge AI represents a significant paradigm shift, leveraging AI within edge computing frameworks to reduce reliance on internet connections and mitigate data latency issues. This approach accelerates data processing, supporting use cases that demand real-time inference. Additionally, as cloud storage costs continue to rise, the feasibility of streaming and storing large volumes of data comes into question. Edge AI offers a compelling solution by performing big data analytics closer to the end device where edge computing is deployed. This paper presents a systematic literature review (SLR) of 85 articles published between 2018 and 2023 within Edge AI. The study provides a comprehensive examination of the analysis of measurement environments and assesses factors applied to Edge AI for big data. It offers taxonomies specific to Edge AI within the big data domain, presents case studies, and outlines the challenges and open issues inherent in Edge AI for big data. [ABSTRACT FROM AUTHOR]

With rapid development and deployment of artificial intelligence methods, the Industrial Internet of Things (IIoT) has highly developed to fast tracing industrial communications and optimizing manufacturing processes. In Industry 5.0, hyper-automation process as one of technological trends navigates industrial entities to intelligent devices of the IIoT, cloud computing, smart robotics, smart agile software and embedded components by high complexity and reliability. By increasing data communication in the IIoT environments and cloud computing, the security and safety of hyper-automation process is also increasingly unstable and challengeable with respect to cyber-attacks, unstructured malwares and abnormal activities. With the diversification and unexpected behaviors of cyber-security threats, traditional cyber-attack detection systems have some critical problems with increasing massive data including unappropriated feature selection and extraction, high computation time in prediction and inaccurate classification models. Due to the above-mentioned challenges, this paper presents a new cloud-based cyber-attack detection architecture based on Ensemble Bagged Trees Detection (EBTD) algorithm for predicting malicious behaviors and types of cyber-attacks for hyper-automation process in the IIoT. The proposed architecture uses Analysis of Variance (ANOVA) and a priority-based feature selection and extraction model to find the optimal features with highly dependent on the network traffic, computation time, malicious behaviors, and types of attacks. Then, experimental results are conducted using the technical data sets UNSW-NB15 and NSL-KDD. The simulation results show that the proposed architecture performance performs better than other case studies and prediction models and effective on optimization of large-scale cyber-attack detection systems for critical hyper-automation process in the IIoT environment. [ABSTRACT FROM AUTHOR]

This paper presents a review of voltammetric and related methods implemented with low-cost, mainly lab-made instrumentation. In recent years, there has been a growing interest in developing low-cost electrochemical instrumentation. Advances in electronics and software have made it easier to access high-performance components for the construction of potentiostats. Currently, lab-made instrumentation for electrochemical research can be very simple, with minimal components to perturb the system and record the response signal. It can also be very advanced, using high-performance electronic components and software development. Several authors have presented the feasibility of designing and manufacturing electrochemical instruments for voltammetry, chronoamperometry, chronopotentiometry, and electrodeposition. This review presents some available options, ranging from basic to advanced. The aim is to provide readers with the full range of possibilities for using their own instruments, especially in cases where access to commercial apparatus is difficult or where specific applications are needed, for example in the Internet of Things or artificial intelligence. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) as an emerging technology is widely used in various applications such as remote healthcare, smart environment, and intelligent transportation systems. It is necessary to address users' concerns about cost, ease of use, privacy, and comprehensive security to grow the popularity of this technology. Intrusion Detection System (IDS) plays an indispensable role in security and preventing unauthorized users to access authorized network resources through analyzing network patterns. Several techniques such as metaheuristic algorithms, machine learning, fuzzy logic, and artificial intelligence algorithms can be applied to increase the accuracy of IDS, feature selection, and network patterns classification. In this paper, a hybrid intrusion detection system based on Support Vector Machine (SVM) and Grey Wolf Optimization (GWO) is presented that utilizes the advantages of these algorithms. In the proposed approach, the support vector machine has been used to train and differentiate anomaly records from normal records and grey wolf optimization has been used to find the kernel function, feature selection, and adjust optimal parameters for the SVM in order to improve the classification. The conducted simulations prove that the proposed approach outperforms in terms of detection accuracy, precision, recall, and F-score on both NSL-KDD and TON_IoT datasets. [ABSTRACT FROM AUTHOR]

The important part of mechanical equipment is rotating machinery, used mostly in industrial machinery. Rolling element bearings are the utmost dominant part in rotating machinery, so even small defects in these components could result in catastrophic system failure and enormous financial losses. Hence, it is crucial to create consistent and affordable condition monitoring and fault diagnosis systems that estimate severity level and failure modes and to create an appropriate maintenance strategy. The studies reveal that the fault diagnostic system focuses on single fault diagnosis of the shaft-bearing system. However, in real scenarios, the occurrence of a single fault is very unlikely. Thus, multifault diagnosis of the shaft-bearing system is of greater significance. This paper aims at steadily and broadly summarizing the development of the intelligent multifault diagnostic and condition monitoring systems. In addition, there is a rapid development of application of Internet of things, cloud computing and artificial intelligence techniques for fault diagnosis. In this paper, we summarize the study of various fault diagnostic system built on the architecture and application of these cutting-edge technologies for predictive maintenance of mechanical equipment. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is crucial for deploying a novel Artificial Intelligence (AI) model for both network and application management. However, using classical centralized learning algorithms in the IoT environment is challenging, given massively distributed private datasets. Advancements in AI have helped us solve various use cases, but it operates under two significant challenges. Firstly, the data exists in separate clusters, and secondly, the current AI has limited data privacy and security. Federated learning (FL) aims to preserve data privacy through distributed learning methods that keep the data in storage silos. Likewise, differential privacy improves data privacy by measuring the privacy loss in communication among the elements of FL. The paper proposes two adaptive approaches for making model training differentially private in a vertical federated environment. The first one uses random feature selection to train different machine learning models, and performance improvement is also proposed. The second approach uses a tree structure, i.e., Classification and Regression Trees, using some defined constraints. Further, we created a scheme to help identify malicious users/devices in a federated network cluster using parity checks for every FL iteration. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and Internet of Things (IoT) technology, increasingly edge devices have entered people's daily lives. However, due to the limited performance of edge devices, complex models can affect the response speed and efficiency of the whole system. Existing research still cannot simultaneously satisfy the demand for accuracy and response speed of edge devices. This paper proposes a lightweight and highly accurate object detection model that uses the Transformer to address edge devices' limited computational capacity and storage space. Specifically, the proposed model adopts the Swin Transformer for multi-scale feature extraction to achieve better global modeling capability. In addition, we propose the Neck module with path aggregation network (PAN), which is designed with a two-feature pyramid structure capable of combining semantic and localization information in order to improve the operational performance by exploiting the underlying location features. A lightweight detection head is then developed using group convolution, fusing the two localization branches and removes the additional decoupling operation. Finally, we conduct comparative experiments on three datasets: the Retail-cabinet dataset, the Roadsign dataset, and the Pascal VOC dataset. Experimental results show that compared with the baseline model, our model achieves an 11.8% improvement in mAP on the Retail-cabinet dataset while reducing Params and FLOPs by 23.19% and 71.50%, respectively. The proposed model effectively reduces the model's computational complexity and improves detection performance, thereby possessing high practical value. This code is released on https://github.com/ydlam/ST-YOLOX. [ABSTRACT FROM AUTHOR]

This paper explores the transformative fusion of Quantum computing, Artificial Intelligence (AI), the Internet of Things (IoT), and advanced optical systems within smart city development. A significant innovation within this study is the concept of "optical IoT," wherein IoT relies on advanced optical technologies, including high-resolution cameras, LiDAR scanners, meters, sensors, and wearables, strategically distributed throughout urban environments for real-time image data acquisition. Traditional smart city models may rely on conventional data acquisition methods that are not real-time or high-resolution, leading to delayed and less accurate urban management decisions. Existing models might use disparate systems for monitoring and management, which can result in inefficient resource allocation and coordination, especially in critical situations like emergency response. In this research Advanced Smart City Architecture (ASCA) this integration empowers the system to excel in tasks such as semantic segmentation, enabling precise identification and categorization of urban elements. Quantum optical systems are employed in quantum-enhanced sensors, such as quantum-enhanced interferometers and atomic clocks. These sensors offer improved precision for measurements like distance, time, and acceleration. The ASCA approach equips city planners, administrators, and emergency responders with real-time urban monitoring and management capabilities. This dynamic system yields numerous advantages, including optimized resource allocation, enhanced traffic management, improved environmental quality, and swift emergency response capabilities. This research underscores the immense potential of ASCA in reshaping urban development and sustainability within smart cities. By harmonizing AI, IoT, and advanced optical systems, this paradigm shift enables smart cities to evolve into more efficient and resilient urban environments. These cities become finely attuned to the ever-evolving needs of their residents, ultimately fostering innovation and progress at an unprecedented scale. Proposed ASCA achieves an impressive 91.98% enhancement in sustainable smart city development when compared to these existing techniques. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides a collaborative infrastructure to communicate smart devices with cloud-edge healthcare applications, medical devices, wearable biosensors, etc. On the other hand, crowd counting as one of computer vision approaches is an emerging topic to detect any objects with static or dynamic mobility in the IoT environments. Smart crowd counting enables pattern recognition for many intelligent applications such as microbiology, surveillance, healthcare systems, crowdedness estimation, and other environmental case studies. According to complicated capturing systems in the IoT environments, crowd counting methods can influence on performance of object detection in the critical case studies using Artificial Intelligence (AI)-based approaches such as machine learning, deep learning, collaborative learning, fuzzy logic and meta-heuristic algorithms. This paper provides a new comprehensive technical analysis for existing AI-based crowd counting approaches in healthcare and medical systems, biotechnology and IoT environments. Meanwhile, it presents a discussion on the existing case studies with respect to analyzing technical aspects and applied algorithms to enhance pattern prediction factors. Finally, some new innovative efforts and challenges are presented for new research upcoming and open issues. [ABSTRACT FROM AUTHOR]

In recent years, transformative technological advances have reshaped countless sectors, including higher education, leaving a lasting and indelible mark. Artificial intelligence (AI) and the Internet of Things (IoT) coming together has proven to be an influential driving force for the development of creative solutions to the ever-evolving challenges faced by educational institutions. Traditional classroom teaching methods have encountered unprecedented hurdles with the widespread integration of the Internet into education. China's commitment to "governing the nation by law" has elevated the need for legal expertise. As society evolves, pedagogical approaches must adapt. Information and communication technology trends, combined with the rise of the IoT, are reshaping education and offering tools to bolster campus security, monitor vital assets, and enrich classroom accessibility. This paper proposes an AI-based hybrid model and an IoT-infused teaching approach personalized for legal education in colleges and universities, employing the decision tree algorithm. The study's focal point is the development of legal professionals possessing the robust capabilities and qualities society seeks, using teaching methods compatible with modern information technology. The foundation of this endeavor lies in the hybrid teaching mode, specifically Internet-based legal research. Within this platform, educators can disseminate instructional materials, videos, and exercises, with the option to enlist teaching assistants to aid in managing student learning. Comparing varied and traditional teaching classes, the passing rates were 96.75% and 95.36%, respectively. Data analysis using decision trees identifies factors influencing these rates. The study also evaluates students' accuracy in five in-class exercises; in the mixed class, correctness rates were 96.4%, 93.5%, 82.5%, 64.2%, and 62.8%. The proposed method is compared with other machine learning algorithms (SVM, KNN, LR, RF) and Decision Trees to evaluate the educational impact of the mixed teaching approach comprehensively. [ABSTRACT FROM AUTHOR]