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Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challenge. In this paper, we present a novel methodology for categorizing AIoMT devices through the application of decentralized processing, referred to as "Federated Learning" (FL). Our approach involves deploying a system on standard IoT devices and labeled IoMT devices for training purposes and attribute extraction. Through this process, we extract and map the interconnected attributes from a global federated cum aggression server. The aim of this terminology is to extract interdependent devices via federated learning, ensuring data privacy and adherence to operational policies. Consequently, a global training dataset repository is coordinated to establish a centralized indexing and synchronization knowledge repository. The categorization process employs generic labels for devices transmitting medical data through regular communication channels. We evaluate our proposed methodology across a variety of IoT, IoMT, and AIoMT devices, demonstrating effective classification and labeling. Our technique yields a reliable categorization index for facilitating efficient access and optimization of medical devices within global servers., (© 2024. The Author(s).)

The world is undergoing a thoughtful revolution with the arrival of the intelligent information era. The central realms accompanying smart living such as transportation, entertainment, healthcare and smart cities are projected to improve service quality assuring a high-end user experience. Future mobile networks are projected to foster the future of ubiquitously connected data-intensive intelligent society powered with complete automation by seamless integrating of all sorts of wireless networks spread over the ground, underwater, air and space. This special issue aims to bring together foremost researchers in academia and engineering from various backgrounds to disseminate to the technical community an outline of emerging technologies, advanced architectures, challenges, open issues and future directions of modern networks in artificial intelligence, internet of things, and blockchain-based applications.

Cancer poses a formidable challenge in the field of medical science, prompting the exploration of innovative and efficient treatment strategies. One revolutionary breakthrough in cancer therapy is Chimeric Antigen Receptor (CAR) T-cell therapy, an avant-garde method involving the customization of a patient's immune cells to combat cancer. Particularly successful in addressing blood cancers, CAR T-cell therapy introduces an unprecedented level of effectiveness, offering the prospect of sustained disease management. As ongoing research advances to overcome current challenges, CAR T-cell therapy stands poised to become an essential tool in the fight against cancer. Ongoing enhancements aim to improve its effectiveness and reduce time and cost, with the integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies. The synergy of AI and IoT could enable more precise tailoring of CAR T-cell therapy to individual patients, streamlining the therapeutic process. This holds the potential to elevate treatment efficacy, mitigate adverse effects, and expedite the overall progress of CAR T-cell therapies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

In today's scenario of India most of the youth is preparing for some competitive exam. If we think behind 15–20 years the number of candidates appearing for competitive exams were in thousands but as the population is increasing exponentially in India day by day the number of candidates is increasing in lacks. We can observe by daily newspapers that most of the competitive exams are facing paper leak problems. Although online examination systems are more effective and secure as compared to offline examination systems because it's not easy to open the question paper before the time starts. On the other hand there are also various consequences where the examination process can be hacked online. But the main issue with online examination process is to lack of resources to conduct parallel examination of millions of candidates, lack of techno enabled exam centers in small cities etc. Our focus is to propose a system in which offline examination can be conducted at most of the govt. and private centers in metro cities as well as small techno backward cities with reduced possibility to leak the paper before commencement of examination. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a new dawn of artificial intelligence (AI), in which machine learning models are constructed in a distributed manner while communicating only model parameters between a centralized aggregator and client internet-of-medical-things (IoMT) nodes. The performance of such a learning technique can be seriously hampered by the activities of a malicious jammer robot. In this paper, we study client selection and channel allocation along with the power control problem of the uplink FL process in IoMT domain under the presence of a jammer from the perspective of long-term learning duration. We map the interaction between the FL network and the jammer in each learning iteration as a Stackelberg game, in which the jammer acts as the leader and the FL network serves as the follower. We consider the client and channel selection as well as the power control jointly as the strategy of this game. Upon formulating the game, we find the joint best response strategy for both types of players by leveraging the difference of convex (DC) programming approach and the dual decomposition technique. Beside the availability of the complete information to both the players, we also study the problem from the perspective that the FL network knows the partial information of the other player. Extensive simulations have been conducted to verify the effectiveness of the proposed algorithms in the jamming game.

Smart health presents an ever-expanding attack surface due to the continuous adoption of a broad variety of Internet of Medical Things (IoMT) devices and applications. IoMT is a common approach to smart city solutions that deliver long-term benefits to critical infrastructures, such as smart healthcare. Many of the IoMT devices in smart cities use Bluetooth technology for short-range communication due to its flexibility, low resource consumption, and flexibility. As smart healthcare applications rely on distributed control optimization, artificial intelligence (AI) and deep learning (DL) offer effective approaches to mitigate cyber-attacks. This paper presents a decentralized, predictive, DL-based process to autonomously detect and block malicious traffic and provide an end-to-end defense against network attacks in IoMT devices. Furthermore, we provide the BlueTack dataset for Bluetooth-based attacks against IoMT networks. To the best of our knowledge, this is the first intrusion detection dataset for Bluetooth classic and Bluetooth low energy (BLE). Using the BlueTack dataset, we devised a multi-layer intrusion detection method that uses deep-learning techniques. We propose a decentralized architecture for deploying this intrusion detection system on the edge nodes of a smart healthcare system that may be deployed in a smart city. The presented multi-layer intrusion detection models achieve performances in the range of 97-99.5% based on the F1 scores.

The papers address the following key areas: ' B I Applications of IoT and digital twin in electrical power systems: A comprehensive survey i b ': This research paper reviews the applications of the Internet of Things (IoT) and digital twin technology in electrical power systems. With the growth of Internet of Things (IoT) techniques, applications have become smarter, and linked gadgets allow them to be used in many parts of power systems. ' B I A new low-cost and low-power industrial Internet of Things infrastructure for effective integration of distributed and isolated systems with smart grids i b ': This research paper provides an Internet of Things (IoT) infrastructure solution based on a newly designed low-cost microcontroller-based IoT remote terminal unit (RTU) to integrate new, old, and conventional sites of existing grids with smart grids. [Extracted from the article]

With the development of Internet of Things technology, the things that machines do instead of humans are becoming more and more complicated. Machine translation has developed rapidly in the past few decades, and the translation system has also been greatly improved. People's life and work are inseparable from machine translation, which brings a lot of convenience to people. But machine translation also has many flaws. Although machine translation can translate long texts in a very short time, its translation quality is quite poor, especially in the face of advanced English such as professional English, terminology, abbreviations, etc. To this end, machine English-assisted translation systems have been developed in recent years. Different from the working principle of machine English translation, machine English-assisted translation is a method of artificial intelligence + human-computer interaction. It uses convolutional neural networks and deep learning to translate words efficiently. The translator puts the original text and the translation into the machine database each time, and the machine can process some English terms, complex sentences, technical English, and other advanced English after continuous learning. Machine English-assisted translation can reduce repeated translations and greatly improve translation quality and translation efficiency. In this paper, the combination of artificial intelligence and machine English-assisted translation is compared with machine English translation, and comparative experiments are carried out by setting different matching degrees. Experiments show that the translation efficiency of machine English-assisted translation is much better than that of machine English translation. As the matching rate increases, the translation efficiency of machine English-assisted translation is higher. When the matching rate is greater than 80%, the translation efficiency is three times that of machine English translation. However, it is slightly insufficient in processing pure, simple statements. It highlights the advantages of machine English-assisted translation in terms of term translation and long complex sentences., Competing Interests: The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2022 Wanfang Zhang and Yuan Tang.)

The Internet of Things (IoT) era is mainly dependent on the word "Smart", such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the result is the Cognitive IoT (CIoT). In the automotive industry, many researchers worked on self-diagnosis systems using deep learning, but most of them performed this process on the cloud due to the hardware limitations of the end-devices, and the devices obtain the decision via the cloud servers. Others worked with simple traditional algorithms of machine learning to solve these limitations of the processing capabilities of the end-devices. In this paper, a self-diagnosis smart device is introduced with fast responses and little overhead using the Multi-Layer Perceptron Neural Network (MLP-NN) as a deep learning technique. The MLP-NN learning stage is performed using a Tensorflow framework to generate an MLP model's parameters. Then, the MLP-NN model is implemented using these model's parameters on a low cost end-device such as ARM Cortex-M Series architecture. After implementing the MLP-NN model, the IoT implementation is built to publish the decision results. With the proposed implemented method for the smart device, the output decision based on sensors values can be taken by the IoT node itself without returning to the cloud. For comparison, another solution is proposed for the cloud-based architecture, where the MLP-NN model is implemented on Cloud. The results clarify a successful implemented MLP-NN model for little capabilities end-devices, where the smart device solution has a lower traffic and latency than the cloud-based solution.

Smart communication has significantly advanced with the integration of the Internet of Things (IoT). Many devices and online services are utilized in the network system to cope with data gathering and forwarding. Recently, many traffic-aware solutions have explored autonomous systems to attain the intelligent routing and flowing of internet traffic with the support of artificial intelligence. However, the inefficient usage of nodes' batteries and long-range communication degrades the connectivity time for the deployed sensors with the end devices. Moreover, trustworthy route identification is another significant research challenge for formulating a smart system. Therefore, this paper presents a smart Random walk Distributed Secured Edge algorithm (RDSE), using a multi-regression model for IoT networks, which aims to enhance the stability of the chosen IoT network with the support of an optimal system. In addition, by using secured computing, the proposed architecture increases the trustworthiness of smart devices with the least node complexity. The proposed algorithm differs from other works in terms of the following factors. Firstly, it uses the random walk to form the initial routes with certain probabilities, and later, by exploring a multi-variant function, it attains long-lasting communication with a high degree of network stability. This helps to improve the optimization criteria for the nodes' communication, and efficiently utilizes energy with the combination of mobile edges. Secondly, the trusted factors successfully identify the normal nodes even when the system is compromised. Therefore, the proposed algorithm reduces data risks and offers a more reliable and private system. In addition, the simulations-based testing reveals the significant performance of the proposed algorithm in comparison to the existing work. [ABSTRACT FROM AUTHOR]

With the in-depth promotion of the national strategy for the integration of artificial intelligence technology and entity development, speech recognition processing technology, as an important medium of human-computer interaction, has received extensive attention and motivated research in industry and academia. However, the existing accurate speech recognition products are based on massive data platform, which has the problems of slow response and security risk, which makes it difficult for the existing speech recognition products to meet the application requirements for timely translation of speech with high response time and network security requirements under the condition of network instability and insecurity. Based on this, this paper studies the analysis model of oral English evaluation algorithm based on Internet of things intelligent algorithm in speech recognition technology. Firstly, based on the automatic machine learning and lightweight learning strategy, a lightweight technology of automatic speech recognition depth neural network adapted to the edge computing power is proposed. Secondly, the quantitative evaluation of Internet of things intelligent classification algorithm and big data analysis in this system is described. In the evaluation, the evaluation method of oral English characteristics is adopted. At the same time, the Internet of things intelligent classification algorithm and big data analysis strategy are used to evaluate the accuracy of oral English. Finally, the experimental results show that the oral English feature recognition system based on Internet of things intelligent classification algorithm and big data analysis has the advantages of good reliability, high intelligence, and strong ability to resist subjective factors, which proves the advantages of Internet of things intelligent classification algorithm and big data analysis in English feature recognition., Competing Interests: The author declares that there are no conflicts of interest., (Copyright © 2022 Nan Xue.)

Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues?, Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not., Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions., Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit.

With the widespread application of IoT technology in the world, the new industry of IoT finance has emerged. Under this new business model, commercial banks and other financial institutions can realize safer and more convenient financial services such as payment, financing and asset management through the application of IoT technology and communication network technology. In the cloud computing model, the local terminal device of IOT will transmit the collected data to the cloud server through the network, and the cloud server will complete the data operation. Cloud computing model can well solve the problem of poor performance of IoT devices, but with the increasing number of IoT terminal devices and huge number of devices accessing the network, cloud computing model is constrained by network bandwidth and performance bottleneck, which brings a series of problems such as high latency, poor real-time and low security. In this paper, based on the new industry of IoT finance which is developing rapidly, we construct a POT (Peaks Over Threshold) over threshold model to empirically analyze the operational risk of commercial banks by using the risk loss data of commercial banks, and estimate the corresponding ES values by using the control variables method to measure the operational risk of traditional commercial banks and IoT finance respectively, and compare the total ES values of the two. This paper adopts the control variable method to reduce the frequency of each type of loss events of operational risk of commercial banks in China respectively., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Yixuan Guo.)

New technology refers to the use of computing machines, AI, Big Data, IoT, deep ‎‎learning, IT, MIS, ‎AIS, knowledge management, capture, manipulate, ‎and retrieve shared knowledge. Therefore, the integration of modern technology, ‎entrepreneurship, and business should be well ‎managed to provide a wide range of high-quality ‎and competitive products and services in societies. The aim of this special issue is to highlight the latest features that blend AI, Big Data, and IoT facilitated and employ them to support the successful growth of businesses. The target of this special issue is to accept high-quality scientific articles that express theory and practical conceptualizations of ideas and critical surveys that cover all aspects pertaining to IoT, AI, and Big Data and their relationship to business success. The special issue received 36 papers, some of which were presented in ICBT'2021 and CBF'2022. All of them were desk evaluated by the editors, followed by at least two blind reviews. [ABSTRACT FROM AUTHOR]

Over the last few decades, remarkable technical advancements, including artificial intelligence, machine learning, big data, blockchain, cloud computing, and the Internet of Things, have emerged. These tools have the ability to change the accounting process. This study aims to conduct a systematic literature review on using the Internet of Things (IoT), blockchain, and eXtensible Business Reporting Language (XBRL) in a single accounting information system (AIS) to enhance the quality of digital financial reports. This paper employs a systematic literature review (SLR) methodology, specifically, by adopting the widely accepted PRISMA technique. The final sample of this study included 309 related studies from 2013 to 2023. Our findings highlight the lack of literature related to the integration of these three types of technologies within a unified AIS. This study is extremely significant because it proposes a new research stream that explores the possibility of integrating IoT, blockchain, and XBRL in a single accounting system, yielding a plethora of benefits to the accounting field. However, the potential benefits of such an integration are evident, including enhanced transparency, real-time reporting capabilities, and improved data security. Our paper's main contribution is that it is the first paper, to the best of our knowledge, to explore the integration of these three technologies. We also identified important gaps in the research and pointed out ways for future research to somehow take a lead in exploring further how this integrated system is affecting accounting practices. [ABSTRACT FROM AUTHOR]

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

The pandemic of COVID-19 is continuing to wreak havoc in 2021, with at least 170 million victims around the world. Healthcare systems are overwhelmed by the large-scale virus infection. Luckily, Internet of Things (IoT) is one of the most effective paradigms in the intelligent world, in which the technology of artificial intelligence (AI), like cloud computing and big data analysis, is playing a vital role in preventing the spread of the pandemic of COVID-19. AI and 5G technologies are advancing by leaps and bounds, further strengthening the intelligence and connectivity of IoT applications, and conventional IoT has been gradually upgraded to be more powerful AI + IoT (AIoT). For example, in terms of remote screening and diagnosis of COVID-19 patients, AI technology based on machine learning and deep learning has recently upgraded medical equipment significantly and has reshaped the workflow with minimal contact with patients, so medical specialists can make clinical decisions more efficiently, providing the best protection not only to patients but also to specialists themselves. This paper reviews the latest progress made in combating COVID-19 with both IoT and AI and also provides comprehensive details on how to combat the pandemic of COVID-19 as well as the technologies that may be applied in the future., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2021 Shu-Wen Chen et al.)

Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant's traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.

In recent years, interest in home energy management systems (HEMS) has grown significantly, as well as the development of Voice Assistants that substantially increase home comfort. This paper presents a novel merging of HEMS with the Assistant paradigm. The combination of both concepts has allowed the creation of a high-performance and easy-to-manage expert system (ES). It has been developed in a framework that includes, on the one hand, the efficient energy management functionality boosted with an Internet of Things (IoT) platform, where artificial intelligence (AI) and big data treatment are blended, and on the other hand, an assistant that interacts both with the user and with the HEMS itself. The creation of this ES has made it possible to optimize consumption levels, improve security, efficiency, comfort, and user experience, as well as home security (presence simulation or security against intruders), automate processes, optimize resources, and provide relevant information to the user facilitating decision making, all based on a multi-objective optimization (MOP) problem model. This paper presents both the scheme and the results obtained, the synergies generated, and the conclusions that can be drawn after 24 months of operation.

In today's world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become "smart" and "cognitive" and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants' data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy. [ABSTRACT FROM AUTHOR]

This paper [[7]] addresses the challenges Intrusion Detection Systems (IDS) face in the Internet of Things (IoT) context due to IoT data's high dimensionality and diversity. The Internet of Things (IoT) has become an increasingly popular technology in recent years, enabling interconnectivity and communication between devices and systems. As a result, there is a growing need for advanced data analysis techniques and artificial intelligence (AI) methods to process, analyze, and extract valuable insights from IoT data. The proposed technique for tracking a moving object of interest in a noisy video using a modified simplest color balance algorithm and a binarization algorithm proved to be effective in building training data for machine learning. [Extracted from the article]

An introduction is presented in which the editor discusses articles in the issue on topics including breast cancer image classification, intelligent engineering management with data mining, and identifying optimal methods for power grid error parameters.

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively. [ABSTRACT FROM AUTHOR]

The Social Internet of Things (SIoT) integrates the Internet of Things (IoT) and social networks, taking into account the social attributes of objects and diversifying the relationship between humans and objects, which overcomes the limitations of the IoT's focus on associations between objects. Artificial Intelligence (AI) technology is rapidly evolving. It is critical to build trustworthy and transparent systems, especially with system security issues coming to the surface. This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT, aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT. Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks. In contrast, this paper proposes a hypergraph generation model that evolves dynamically over time, where only the number of nodes is fixed. It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges. As new nodes are added, the model can promptly reflect changes in entities and relationships within SIoT. Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks. We also evaluate the vulnerability of the hypergraph under different attack strategies, which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future. [ABSTRACT FROM AUTHOR]

Various technologies with AI (Artificial Intelligence), DS (Data Science), and/or IoT (Internet of Things) have been starting to be pervasive in e-tourism (i.e., smart tourism). However, most of them for a target (e.g., what to do in such a tourism spot as Hikone Castle) utilize their "typical/major signals" (e.g., taking a photo) as summarized knowledge based on "The Principle of Majority", and tend to filter out not only their noises but also their valuable "peculiar/minor signals" (e.g., view Sawayama Castle) as instantiated knowledge. Therefore, as a challenge to salvage not only "typical signals" but also "peculiar signals" without noises for e-tourism, this paper compares various methods of ML (Machine Learning) to text-classify a tweet as being a "tourism tweet" or not, to precisely mine tourism tweets as not summarized but instantiated knowledge. In addition, this paper proposes a MAS (Multi-Agent Simulation), powered with artisoc, for visualizing "tourism tweets", including not only "typical signals" but also "peculiar signals", whose number can be enormous, as not summarized but instantiated knowledge, i.e., instances of them without any summarization, and validates the effects of the proposed MAS by conducting some experiments with subjects. [ABSTRACT FROM AUTHOR]

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The integration of IoT systems and AI technology is very suitable for interconnected things to enhance intelligence, thus the artificial intelligence of things (AIoT), which enables the establishment of innovative IoT systems and applications for the modern smart city and smart home. The Internet of Things (IoT) consists of interconnected things with built-in and function-oriented sensors, essentially constituting a network of physical devices. The paper titled "Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency" by F. A. Almalki et al. proposes a drone system with AI framework to help clean solar panels mounted on the top of buildings and enhance their efficiency. [Extracted from the article]

Recently, Digital Twin (DT) has a growth revolution by increasing Artificial Intelligence (AI) techniques and relative technologies as the Internet of Things (IoT). They may be considered as the panacea for DT technology for various applications in the real world such as manufacturing, healthcare, and smart cities. The integration of DT and AI is a new avenue for open research in the upcoming days. However, for exploring the issues of developing Digital Twins, there are interesting in identifying challenges with standardization ensures future developments in this innovative theme. This paper first presents the Digital Twins concept, challenges, and applications. Afterward, it discusses the incorporation of AI and DT for developing various IoT-based applications with exploring the challenges and opportunities in this innovative arena. Then, developing tools are presented for exploring the digital twins' system implementation. Further, a review of recent DT-based AI approaches is presented. Finally, a discussion of open research directions in this innovative theme is presented. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms. [ABSTRACT FROM AUTHOR]

During its existence, the retail industry underwent profound transformations. Over the past three decades the retail industry has incorporated new technologies, namely Artificial Intelligence, Internet of Things and Big Data Analytics. All these advancements deeply intensified competition in global retailing. The COVID-19 pandemic had a significant impact on the businesses of global retailers. The purpose of the paper is to present and briefly analyse the evolution of the largest global retailers in terms of their retail revenues in the COVID-19 pandemic period 2020-2021. In this respect, a qualitative research method was used. The results of the paper show that global retailers are dominating the retail industry. They obtained impressive retail revenues even during the COVID-pandemic period 2020-2021. Walmart, the American giant, was by far the largest global retailer in that period. [ABSTRACT FROM AUTHOR]

COVID-19 outbreak has caused a devastating impact on the daily lives of people, public health, and economic progress of infected countries. It has become a leading cause of substantial mortality and morbidity around the world. The emergence of new variants of virus has posed severe challenges for humanitarian society. Information and Communication Technology (ICT) has played a vital role in this pandemic and offered various promising innovations to control its dissemination. The current research study presents a scientometric analysis on the literature of ICT-assisted COVID-19 research. In this paper, ICT has been classified into six major categories; artificial intelligence and medical imaging, mobile technology, ubiquitous computing, big data analytics, social media platforms, and printing technology. It extensively examines the role of these technologies in COVID-19 by applying various empirical approaches such as co-citation analysis, publication and citation behavior analysis, participating nations, and knowledge mapping of scientific literature using visualization tool CiteSpace. Furthermore, it provides a visual approach to identify developing paths, evolution trends, research hotspots, cluster analysis, and potential future directions in medical informatics. [ABSTRACT FROM AUTHOR]

Learning-driven ubiquitous mobile edge computing: Network management challenges for future generation Internet of Things Keywords: artificial intelligence; Internet of Things; machine learning; mobile edge computing; network Management EN artificial intelligence Internet of Things machine learning mobile edge computing network Management 1 4 4 09/13/23 20230901 NES 230901 Ubiquitous edge computing facilitates efficient cloud services near mobile devices, enabling mobile edge computing (MEC) to offer services more efficiently by presenting storage and processing capability within the proximity of mobile devices and in general IoT domains. Fifth, I Jafar et al. i proposes "A blockchain-enabled security management framework for mobile edge computing", which guarantees secure data storage and includes blockchain features like immutability and traceability. [Extracted from the article]

In recent years, Internet of Things security incidents occur frequently, which is often accompanied by malicious events. Therefore, anomaly detection is an important part of Internet of Things security defense. In this paper, we create a process whitelist based on the K-Core decomposition method for detecting anomalous processes in IoT devices. The method first constructs an IoT process network according to the relationships between processes and IoT devices. Subsequently, it creates a whitelist and detect anomalous processes. Our work innovatively transforms process data into a network framework, employing K-Core analysis to identify core processes that signify high popularity. Then, a threshold-based filtering mechanism is applied to formulate the process whitelist. Experimental results show that the unsupervised method proposed in this paper can accurately detect anomalous processes on real-world datasets. Therefore, we believe our algorithm can be widely applied to anomaly process detection, ultimately enhancing the overall security of the IoT. [ABSTRACT FROM AUTHOR]

A Multilevel Inverter (MLI) meets the power requirements of most power electronic applications. It can provide an AC supply with better power quality. The higher number of levels in an MLI results in better power quality output. The fundamental question that comes to mind is what the optimal number of levels can be in designing an MLI for better performance. This paper aims to provide a comparison of three-, five-, and seven-level schemes based on a "Diode Clamped MLI" and will analyze their performance on the reduction of Total Harmonic Distortion (THD). [ABSTRACT FROM AUTHOR]

A dual-band UWB coplanar waveguide monopole antenna is presented in this paper. The antenna proposed in this paper consists of an FR4 substrate, two ground planes, and a rectangular patch. The size of the antenna proposed is 24 x 25 x 1 mm³ with a slit of length and width of 5.7 and 0.5 mm, respectively. We received two operating bands, a C- and Ku-band, with both Bi and Omni-directional radiation patterns. The bandwidth values of the first (3.65-7.49) GHz and second (11.5-13.4) GHz band of the proposed antenna are 3.9 GHz and 1.7 GHz, respectively. The proposed antenna operating at a frequency 5.8 GHz has a maximum bandwidth of 4.5 GHz and minimum bandwidth of 1.6 GHz. The characteristics of this antenna show that the antenna proposed in this paper is suitable for dual-band and UWB communication systems. We achieved a wide bandwidth by improving the ground plane. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) have emerged as a promising technology for enhancing road safety and mobility. However, designing AVs involves various critical aspects, such as software and system requirements, that must be carefully addressed. This paper investigates safety-aware approaches for AVs, focusing on the software and system requirements aspect. It reviews the existing methods based on software and system design and analyzes them according to their algorithms, parameters, evaluation criteria, and challenges. This paper also examines the state-of-the-art artificial intelligence-based techniques for AVs, as AI has been a crucial element in advancing this technology. This paper reveals that 63% of the reviewed studies use various AI methods, with deep learning being the most prevalent (34%). The article also identifies the current gaps and future directions for AV safety research. This paper can be a valuable reference for researchers and practitioners on AV safety. [ABSTRACT FROM AUTHOR]

The dominant intrusion detection models in internet of things industrial internet of things cybersecurity use network-based datasets. The Modbus protocol is one of the most often targeted protocols and cyberattacks against IoT/IIoT devices have grown to be a major threat in recent years. Due to the intricacy of the protocol and the quick evolution of cyber threats, detecting these attacks using conventional techniques might be difficult. This paper proposes an architecture that consistently outperforms the state-of-the-art methods of performing intrusion Detection that includes binary classification of whether an intrusion occurred or not and multi-class classification that classifies the different types of attacks using an embedding layer in a neural network to model the register values. The best accuracy results were obtained with a convolutional neural network, with an accuracy of 98.91% in the Modbus Binary dataset, a fully connected neural network with an accuracy of 98.06% in the multi-class classification of the Modbus dataset, and long short-term memory neural networks with an accuracy of 99.97%, 99.7%, and 80.20% in Binary, multi-class, and multi-class sub-categories, respectively which conclude that the proposed architecture performs consistently better than the control NN. Three NN are designed with and without the proposed architecture. All experiments performed in this paper conclude that the proposed architecture performs consistently better than the control NN. This paper shows that a NN with an embedding function can effectively be used to model whether an attack occurred on a device and the class of attack that occurred. This network can be utilized in the future to lessen DoS attacks and other types of network attacks. The network will be able to protect itself against a lot of damage if attacks can be predicted either before they occur or at the same moment they are launched. [ABSTRACT FROM AUTHOR]