Utilizing information from multiple sources is a preferred and more precise method for medical experts to confirm a diagnosis. Each source provides critical information about the disease that might otherwise be absent in other modalities. Combining information from various medical sources boosts confidence in the diagnosis process, enabling the creation of an effective treatment plan for the patient. The scarcity of medical experts to diagnose diseases motivates the development of automatic diagnoses relying on multimodal data. With the progress in artificial intelligence technology, automated diagnosis using multimodal fusion techniques is now possible. Nevertheless, the concept of multimodal medical diagnosis is still new and requires an understanding of the diverse aspects of multimodal data and its related challenges. This review article examines the various aspects of multimodal medical diagnosis to equip readers, academicians, and researchers with necessary knowledge to advance multimodal medical research. The chosen articles in the study underwent thorough screening from reputable journals and publishers to offer high-quality content to readers, who can then apply the knowledge to produce quality research. Besides, the need for multimodal information and the associated challenges are discussed with solutions. Additionally, ethical issues of using artificial intelligence in medical diagnosis is also discussed.

Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., computational grids is a NP-complete problem. Many researchers have proposed several task scheduling algorithms on grids based on formulating and solving it as an optimization problem with different objective functions such as makespan, cost, energy etc. Further to address the requirements/demands/needs of the users (lesser cost, lower latency etc.) and grid service providers (high utilization and high profitability), a task scheduler needs to be designed based on solving a multi-objective optimization problem due to several trade-offs among the objective functions. In this direction, we propose an efficient multi-objective task scheduling framework to schedule computationally intensive tasks on heterogeneous grid networks. This framework minimizes turnaround time, communication, and execution costs while maximizing grid utilization. We evaluated the performance of our proposed algorithm through experiments conducted on standard, random, and scientific task graphs using the GridSim simulator.

Recently, various technologies for utilizing unmanned aerial vehicles have been studied. Drones are a kind of unmanned aerial vehicle. Drone-based mobile surveillance systems can be applied for various purposes such as object recognition or object tracking. In this paper, we propose a mobility-aware dynamic computation offloading scheme, which can be used for tracking and recognizing a moving object on the drone. The purpose of the proposed scheme is to reduce the time required for recognizing and tracking a moving target object. Reducing recognition and tracking time is a very important issue because it is a very time critical job. Our dynamic computation offloading scheme considers both the dwell time of the moving target object and the network failure rate to estimate the response time accurately. Based on the simulation results, our dynamic computation offloading scheme can reduce the response time required for tracking the moving target object efficiently.

The healthcare industry, aided by technology, leverages the Internet of Things (IoT) paradigm to offer patient/user-related services that are ubiquitous and personalized. The authorized repository stores ubiquitous data for which access-level securities are granted. These security measures ensure that only authorized entities can access patient/user health information, preventing unauthorized entries and data downloads. However, recent sophisticated security and privacy attacks such as data breaches, data integrity issues, and data collusion have raised concerns in the healthcare industry. As healthcare data grows, conventional solutions often fail due to scalability concerns, causing inefficiencies and delays. This is especially true for multi-key authentication. Dependence on conventional access control systems leads to security flaws and authorization errors caused by static user behaviour models. This article introduces an Opportunistic Access Control Scheme (OACS) for leveraging access-level security. This approach is a defendable access control scheme in which the user permissions are based on their requirement and data. After accessing the healthcare record, a centralized IoT security augmentation and assessment is provided. The blockchain records determine and revoke the access grant based on previous access and delegation sequences. This scheme analyses the possible delegation methods for providing precise users with interrupt-free healthcare record access. The blockchain recommendations are analyzed using a trained learning paradigm to provide further access and denials. The proposed method reduces false rates by 11.74%, increases access rates by 13.1%, speeds up access and processing by 12.36% and 13.23%, respectively, and reduces failure rates by 9.94%. The OACS decreases false rates by 10.64%, processing time by 15.62%, and failure rates by 10.95%.