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This study explores integrating blockchain technology into the Internet of Medical Things (IoMT) to address security and privacy challenges. Blockchain's transparency, confidentiality, and decentralization offer significant potential benefits in the healthcare domain. The research examines various blockchain components, layers, and protocols, highlighting their role in IoMT. It also explores IoMT applications, security challenges, and methods for integrating blockchain to enhance security. Blockchain integration can be vital in securing and managing this data while preserving patient privacy. It also opens up new possibilities in healthcare, medical research, and data management. The results provide a practical approach to handling a large amount of data from IoMT devices. This strategy makes effective use of data resource fragmentation and encryption techniques. It is essential to have well-defined standards and norms, especially in the healthcare sector, where upholding safety and protecting the confidentiality of information are critical. These results illustrate that it is essential to follow standards like HIPAA, and blockchain technology can help ensure these criteria are met. Furthermore, the study explores the potential benefits of blockchain technology for enhancing inter-system communication in the healthcare industry while maintaining patient privacy protection. The results highlight the effectiveness of blockchain's consistency and cryptographic techniques in combining identity management and healthcare data protection, protecting patient privacy and data integrity. Blockchain is an unchangeable distributed ledger system. In short, the paper provides important insights into how blockchain technology may transform the healthcare industry by effectively addressing significant challenges and generating legal, safe, and interoperable solutions. Researchers, doctors, and graduate students are the audience for our paper., (© 2024. The Author(s).)

The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms.

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers have no proper knowledge to select which crop is suitable to grow according to the environmental factors and soil characteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sector of the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning, and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC), and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggesting a suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, firstmodule uses static data and the second module takes hybrid data collection (IoT-based real-time data and manual data) with machine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the yield. Python is used to train themodel that predicts the crop. This systemproposed an intelligent and low-cost solution for the farmers to process the data and predict the suitable crop. We implemented the proposed system in the field. The efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop. [ABSTRACT FROM AUTHOR]

In the past few years, privacy concerns have grown, making the financial models of businesses more vulnerable to attack. In many cases, it is hard to emphasize the importance of monitoring things in real-time with data from Internet of Things (IoT) devices. The people who make the IoT devices and those who use them face big problems when they try to use Artificial Intelligence (AI) techniques in real-world applications, where data must be collected and processed at a central location. Federated learning (FL) has made a decentralized, cooperative AI system that can be used by many IoT apps that use AI. It is possible because it can train AI on IoT devices that are spread out and do not need to share data. FL allows local models to be trained on local data and share their knowledge to improve a global model. Also, shared learning allows models from all over the world to be trained using data from all over the world. This article looks at the IoT in all of its forms, including "smart" businesses, "smart" cities, "smart" transportation, and "smart" healthcare. This study looks at the safety problems that the federated learning with IoT (FL-IoT) area has brought to market. This research is needed to explore because federated learning is a new technique, and a small amount of work is done on challenges faced during integration with IoT. This research also helps in the real world in such applications where encrypted data must be sent from one place to another. Researchers and graduate students are the audience of our article. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website's content as a technical resource and reference. [ABSTRACT FROM AUTHOR]

In this paper, new LC lumped components and composite lines are used to create a filtering branch line coupler (FBLC) with a small size and wide suppression band. New composite lines are proposed using applied LC lumped components, which are used as the coupler main branches. The proposed FBLC suppresses second to sixth harmonics with high attention level and provides a wide stopband from 1.6 GHz to 5 GHz with more than 20 dB of attention. The presented coupler is analyzed, designed, simulated, and implemented. The measured results show that the proposed FBLC correctly operates at 800 MHz with less than 0.25 dB of insertion loss. In addition, more than 29 dB of return loss and isolation is measured at operating frequency, which shows the correct performance of the proposed design. The size of the proposed FBLC is equal to 23.7 mm × 25.5 mm (0.086λ × 0.093λ), which shows an 87% size reduction. The proposed FBLC with the designed frequency can be used in the communication systems for narrow-band Internet of things (NB-IoT) and traffic control radar applications. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technologies continuously expand with time due to the advances in automated, connected device technology, mobility, and wide access to information. IoT is the collection of many linked devices due to their unique features, such as scalability, maintainability, fault tolerance, reliability, accuracy, and much more. With the growing number of hybrid devices in large organizations, security and privacy concerns are becoming more challenging. Security is essential for protecting the hardware, network aspects of devices, and information access from unauthorized entities. Most of the security methods and procedures provided by researchers are based on existing Internet security practices. The top-ranking authentication feature categories included the most compatible and common authentication feature for all types of IoT-based devices as an elementary security requirement for protection from unauthorized access. The future challenge is to address the incompatibility of the authentication feature with IoT devices based on appropriate technologies. AI and machine learning are also implementable in order to detect the vulnerability of IoT devices and inform the concerned operator or administration for protection. This research highlights the authentication feature of IoT devices from literature studies and evaluates the significant feature using COPRAS approach to assist organization in enhancing security of IoT devices. [ABSTRACT FROM AUTHOR]