Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

Due to recent advancements in quantum drones, the Internet of Quantum Drones (IoQDs), and Drone-to-Satellite connectivity, several advantages have been anticipated for real-time applications. This work examines quantum computing issues, including quantum data processing, techniques, circuits, and algorithms important for quantum drones or their networks. Here, we discussed the current research trends on quantum computing, quantum-safe computing, or post-quantum cryptography important to quantum networks, followed by the numerous advantages, limitations, future advancements, and research issues connected with quantum technologies, drones, and their network. This work has also prepared a taxonomy of quantum-related areas depending upon the logic of their learning, followed by a review of each of these areas. We review the most recent work over quantum algorithms used in various-quantum-related areas and networks, the role of quantum satellites for drone-based networks and communications, how quantum artificial intelligence and quantum machine learning are important for quantum drones, networks and futuristic applications, quantum attacks, quantum genetic algorithms, and the importance of post-quantum and quantum-safe cryptography. The challenges and research directions in these domains are explored as well. Lastly, this work presents an overview of the current state of knowledge in various promising technologies that are recently found to be important for quantum drones and networks.

The internet of medical things (IoMT) is a smart medical device structure that includes apps, health services, and systems. These medical equipment and applications are linked to healthcare systems via the internet. Because IoT devices lack computational power, the collected data can be processed and analyzed in the cloud by more computationally intensive tools. Cloud computing in IoMT is also used to store IoT data as part of a collaborative effort. Cloud computing has provided new avenues for providing services to users with better user experience, scalability, and proper resource utilization compared to traditional platforms. However, these cloud platforms are susceptible to several security breaches evident from recent and past incidents. Trust management is a crucial feature required for providing secure and reliable service to users. The traditional trust management protocols in the cloud computing situation are centralized and result in single-point failure. Blockchain has emerged as the possible use case for the domain that requires trust and reliability in several aspects. Different researchers have presented various blockchain-based trust management approaches. This study reviews the trust challenges in cloud computing and analyzes how blockchain technology addresses these challenges using blockchain-based trust management frameworks. There are ten (10) solutions under two broad categories of decentralization and security. These challenges are centralization, huge overhead, trust evidence, less adaptive, and inaccuracy. This systematic review has been performed in six stages: identifying the research question, research methods, screening the related articles, abstract and keyword examination, data retrieval, and mapping processing. Atlas.ti software is used to analyze the relevant articles based on keywords. A total of 70 codes and 262 quotations are compiled, and furthermore, these quotations are categorized using manual coding. Finally, 20 solutions under two main categories of decentralization and security were retrieved. Out of these ten (10) solutions, three (03) fell in the security category, and the rest seven (07) came under the decentralization category.

Providing an identity solution is essential for a reliable blockchain-based land registry system. A secure, privacy-preserving, and efficient identity solution is essential but challenging. This paper examines the current literature and provides a systematic literature review in three stages based on the three research questions (RQ) that show the assessment and interpretation process step by step. Based on the parameters and RQ specified in the research methodology section, a total of 43 primary articles have been selected from the 251 articles extracted from various scientific databases. The majority of these articles are concerned with evaluating the existing self-sovereign identity (SSI) solutions and their role in the blockchain-based land registry system to address the compliance issues in the existing SSI solutions with SSI principles and find the best possible SSI solution to address the identity problems in the land registry. The existing digital identity solutions cannot handle the requirements of the identity principle and are prone to various limitations like centralization and dependency on third parties that further augment the chance of security threats. SSI has been designed to overcome these limitations and provide a secure, reliable, and efficient identity solution that gives complete control to the users over their personal identity information (PII). This paper reviews the existing SSI solutions, evaluates them based on the SSI principles, and comes up with the best possible SSI solution for a blockchain-based land registry system. It further provides a detailed investigation of each SSI solution to present its functionalities and limitations for further improvement.

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through the utilisation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol.

Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance for successful treatment. However, these tests can be expensive and invasive and the results have to be interpreted by experts. Genetic algorithms (GAs) are metaheuristics that belong to the class of evolutionary algorithms. GAs can find the optimal or near-optimal solutions in huge, difficult search spaces and are widely used for search and optimization. This makes them ideal for detecting cancer by creating models to interpret the results of tests, especially noninvasive. In this article, we have comprehensively reviewed the existing literature, analyzed them critically, provided a comparative analysis of the state-of-the-art techniques, and identified the future challenges in the development of such techniques by medical professionals.