The Internet of Things (IoT) and artificial intelligence (AI) enabled IoT is a significant paradigm that has been proliferating to new heights in recent years. IoT is a smart technology in which the physical objects or the things that are ubiquitously around us are networked and linked to the internet to deliver new services and enhance efficiency. The primary objective of the IoT is to connect all the physical objects or the things of the world under a common infrastructure, allowing humans to control them and get timely, frequent updates on their status. These things or devices connected to IoT generate, gather and process a massive volume of binary data. This massive volume of data generated from these devices is analyzed and learned by AI algorithms and techniques that aid in providing users with better services. Thus, AI-enabled IoT or artificial IoT (AIoT) is a hybrid technology that merges AI with IoT and is capable of simplifying complicated and strenuous tasks with ease and efficiency. The various machine learning (ML) and deep learning (DL) algorithms in IoT are necessary to ensure the IoT network’s improved security and confidentiality. Furthermore, this paper also surveys the various architectures that form the backbone of IoT and AIoT. Moreover, the myriad state-of-the-art ML and DL-based approaches for securing IoT, including detecting anomalies/intrusions, authentication and access control, attack detection and mitigation, preventing distributed denial of service (DDoS) attacks, and analyzing malware in IoT, are also enlightened. In addition, this work also reviews the role of AIoT in optimizing network efficiency, securing IoT infrastructures, and addressing key challenges. Furthermore, it explores cutting-edge technologies like blockchain, 6G-enabled AIoT, federated learning (FL), and hyperdimensional (HD) computing, indicating their potential in advancing IoT and AIoT-driven applications within sectors like healthcare, autonomous systems, and industrial automation. Therefore, based on the plethora of prevailing significant works, the objective of this manuscript is to provide a comprehensive survey that expounds on AIoT in terms of security, architecture, applications, emerging technologies, and challenges.

The Know Your Customer (KYC) process is a fundamental prerequisite for any financial institution’s compliance with the regulatory framework. Blockchain technology has emerged as a revolutionary solution to enhance the effectiveness of the KYC procedure. It ensures that the KYC process is transparent, secure, and immutable, thereby offering a robust solution to combat fraudulent activities. The potential of blockchain technology in revolutionizing the KYC process has been acknowledged globally. Blockchain technology provides a decentralized platform for storing customer data, enabling financial institutions to access the information seamlessly. Using ethereum blockchain technology in KYC procedures can enhance the efficiency of financial institutions, significantly reducing the time and cost associated with the process. This work aims to provide a viable and sustainable solution to the challenges that banks experience in implementing KYC procedures and onboarding new customers. The proposed solution involves the central bank maintaining a comprehensive register of all registered banks while closely monitoring their adherence to the existing regulations governing KYC and customer acquisition.

Recent years have seen significant studies into applying machine learning techniques for stock price prediction. However, most existing work in this field focuses on examining historical stock price data for forecasting stock prices, ignoring the role of public sentiments and mood on the stock market. In this work, a new approach is introduced that considers the sentiment factor along with the historical stock price to increase the precision of stock price forecasting. The proposed system includes a deep neural network that takes historical stock price information and sentiment analysis of news headlines as input characteristics. The system evaluates the framework's performance using the dataset of news headlines and stock prices of six different industries. There currently needs to be more literature in the field of stock price prediction, which incorporates both historical stock price data and sentimental data. Hence, the proposed hybrid approach for stock price prediction using sentiment and technical analysis significantly contributes to estimating the stock price.

This survey paper provides a comprehensive overview of integrating Multiple-Input Multiple-Output (MIMO) with Intelligent Reflecting Surfaces (IRS) in wireless communication systems. IRS is known as reconfigurable metasurfaces, have emerged as a transformative technology to enhance wireless communication performance by manipulating the propagation environment. This work delves into the fundamental concepts of MIMO and IRS technologies, exploring their benefits and applications. It subsequently investigates the synergies of resource allocation and energy efficiency that emerge when these technologies are combined, elucidating the IRS improved in MIMO systems through signal manipulation and beamforming. Through an in-depth analysis of various techniques and cutting-edge algorithms in resource allocation and energy efficiency can explore the key research areas such as optimization techniques, beamforming strategies and practical implementation consideration. Furthermore, it provides open research directions, individually addressing topics such as limitations of resource allocation and energy efficiency in the MIMO IRS system. This paper offers insights into MIMO-enabled IRS systems challenges and future trends. Through presenting a consolidated view of the current state-of-the-art, this survey underscores their potential to revolutionize wireless communication paradigms, ushering in an era of enhanced connectivity, spectral efficiency and improved coverage.

Maintaining optimal tip clearance or tip gap is challenging in the Gas Turbine Engine (GTE). Meanwhile, the rotor blades should not rub the casing. When the capacitive sensor is used to measure the tip clearance in the form of a single peak signal for every blade pass, often the signal will be affected by stationary and non-stationary noises during engine running. This leads to distorted multiple peaks for every blade pass. In this work, the wavelet denoising technique removes the noise, and then the peak frequency in each blade pass is detected through a short-time Fourier transform (STFT). Finally, the cubic spline interpolation technique is employed to obtain the continuous time domain blade pass signal. This work uses the compressor stage of GTE data collected from the Gas Turbine Research Establishment (GTRE), DRDO, Bangalore. From the experimental analysis, this paper observes that the proposed methodology produces substantial results compared to the expected results.

Establishing a well-functioning Supply Chain Management (SCM) system is paramount during challenging times such as pandemics, natural disasters, and international conflicts. The complexity of global supply chains necessitates efficient systems, procedures, and personnel to ensure optimal results. Poor coordination among entities can lead to increased counterfeit products, increased ocean transportation costs, more expensive freight brokerage, bottlenecks in cargo flow, congestion, and complications in product accountability. To ensure a smooth and hassle-free operation, it’s essential to maintain unambiguity and accuracy throughout every process. Therefore, it is vital to have effective systems, procedures, and personnel in place for SCM. The challenges encountered in SCM can be effectively tackled by utilizing blockchain technology. The architecture of blockchain technology is characterized by its distributed, decentralized and robust safety measures, which guarantee the integrity of data storage and its distribution across a meticulously organized ledger. Users can confidently rely on this innovative design’s transparency, reliability, and safety. Implementing blockchain technology carries immense potential in bolstering safety and privacy measures in diverse sectors, including agriculture, healthcare, Goods and Services Tax (GST), academics, e-voting and automobile. This investigation delves into the practical applications of blockchain technology for SCM. It thoroughly analyses existing research and literature to uncover the latest advancements and potential future breakthroughs in this area.