The Internet of Medical Things enables real-time health monitoring through wireless sensors and embedded computing, but raises critical concerns regarding patient data confidentiality in the era of quantum computing. Classical public-key cryptosystems, including RSA and ECC, remain vulnerable to Shor’s algorithm, which efficiently solves integer factorization and discrete logarithm problems on quantum computers. Although the National Institute of Standards and Technology (NIST) has standardized post-quantum cryptographic algorithms that are resistant to quantum cyber threats, the available solutions are either computationally burdensome for resource-constrained IoMT devices or compromise the authentication of the end-to-end verification chain via proxy-based approaches. This paper proposes a zero trust-based post-quantum framework that leverages the power of edge-based computing offloading while maintaining the cryptographic verification chain. The architectural design employs CRYSTALS-Kyber-512 for key encapsulation, CRYSTALS-Dilithium-2 for dual-layer digital signatures, and AES-256-GCM for symmetric key encryption. The Quantum-Secure Edge Server (QSES) carries out computationally expensive post-quantum computations, while digital signatures from devices are verifiable at application servers, thereby allowing for non-repudiation, which is critical for medical liability applications. These security proofs show Existential Unforgeability under Adaptive Chosen Message Attacks (EUF-CMA) in the Quantum Random Oracle Model (QROM). They are based on the standard assumptions of Module-LWE and Module-SIS hardness and were checked again using the Scyther tool and a full end-to-end security analysis. Performance evaluation demonstrates 1.87 ms client-side computation with 8,416 bytes communication overhead, achieving faster processing than full on-device post-quantum implementations while maintaining end-to-end authentication properties absent in existing edge-offloading schemes.

ABSTRACT Bio‐inspired computer‐aided diagnosis (CAD) has garnered significant attention in recent years due to the inherent advantages of bio‐inspired evolutionary algorithms (EAs) in handling small datasets with elevated precision and reduced computational complexity. Traditional CAD models face limitations as they can only be developed post‐outbreak, relying on datasets that become available during such events such as the COVID‐19 pandemic. The scarcity of data for emerging diseases poses a substantial challenge to achieving elevated precision with conventional deep‐learning algorithms. Furthermore, even when datasets are available, employing deep learning for class‐based classification is arduous, necessitating model retraining, in this paper, we propose a novel hybrid algorithm that leverages the strengths of the crow search algorithm (CSA) and the spider monkey optimization (SMO) algorithm to create an optimised spider monkey crow search (OSM‐CS) algorithm. We developed a CAD tool that maps each input CT image to a high‐dimensional vector by extracting four categories of features: high contrast, polynomial decomposition, textural, and pixel statistics. The proposed OSM‐CS algorithm is employed as a feature selection method. Our experimental results demonstrate the effectiveness of the OSM‐CS algorithm, achieving an impressive accuracy of 98.2% when coupled with an AdaBoost classifier for multi‐class classification and 99.93% for binary classification. This performance surpasses that of state‐of‐the‐art (SOTA) deep learning models and recently published hybrid algorithms, underscoring the potential of the OSM‐CS algorithm as a powerful tool in the realm of CAD.

Summary As the demand for cloud storage systems increases, ensuring the security and integrity of cloud data becomes a challenge. Data uploaded to cloud systems are vulnerable to numerous sorts of assaults, which must be handled appropriately to avoid data tampering issues. In addition, quantum computers are expected to be introduced soon, which may face multiple security issues by destroying all traditional cryptosystems. This work introduces a quantum‐resistant blockchain centered data integrity verification system with the use of several techniques. Initially, the keys and signatures are generated by the users with the help of the lattice‐based blind signature algorithm (L_BSA), which is a combination of lattice cryptography and a blind signature algorithm. From the generated random keys, the most optimal key is then selected by the Puzzle Optimization Algorithm (POA), which is then made available to the encryption phase. Then, the upgraded Merkle tree‐assisted vacuum filter (Vac‐UMT) algorithm is executed to accomplish the encryption task. Then the data are converted into blocks using blockchain technology and uploaded to the cloud. When receiving the audit requests, the verification process is carried out, and the evidence report is generated for the users. The proposed work is simulated in JAVA and assessed with the UNSW‐NB15 dataset, and the outcomes demonstrated that the system is highly efficient and secure.

Social media platforms are extensively used for expressing opinions or conveying information.The information available on such platforms can be used for various humanitarian and disaster-related tasks as distributing messages in different formats through social media is quick and easy.Often this useful information during disaster events goes to waste as efficient systems don't exist which can turn these unstructured data into meaningful format which can ultimately assist aid agencies.In disaster identification and assessment, information available is naturally multimodal, however, most existing work has been solely focused on single modalities e.g.images or texts separately.When information from different modalities are integrated, it produces significantly better results.In this paper, we have explored different models which can lead to the development of a system that deals with multimodal datasets and can perform sequential hierarchical classification.Specifically, we aim to find the damage and its severity along with classifying the data into humanitarian categories.The different stages in the hierarchical classification have had their respective models selected by researching with many different modality specific models and approaches of multimodal classification including multi task learning.The hierarchical model can give results at different abstraction levels according to the use cases.Through extensive quantitative and qualitative analysis, we show how our system is effective in classifying the multimodal tweets along with an excellent computational efficiency and assessment performance.With the help of our approach, we aim to support disaster management through identification of situations involving humanitarian tragedies and aid in assessing the severity and type of damage.

Summary A high‐performance heterogeneous computing environment provides computing resources for efficient computation of scientific Big Data workflow applications. A Big Data workflow application comprises thousands of interdependent tasks with precedence constraints. Basically, the performance of heterogeneous computing systems mainly depends on the workflow scheduling algorithms. These workflow scheduling algorithms are considered an NP‐complete problem. In this article, a List Scheduling with Task Duplication (LSTD) algorithm is proposed that efficiently minimizes the makespan of workflow applications. The LSTD introduces task duplication strategy in the list scheduling algorithm without increasing the overall time complexity. The overall functionality of LSTD mainly consists of three phases. In the first phase, it calculates the rank of the tasks for deciding the scheduling order. The next step is responsible for duplicating the entry task on the processor only if it increases the overall efficiency and avoids processor overloading. Finally, in the last step, the processor is assigned to the tasks based on the popular insertion‐based policy that attempts to insert the task among two earlier assigned tasks on a given processor in earliest idle time. In order to verify the usefulness of the proposed algorithm, several existing well‐known algorithms, such as Heterogeneous Earliest Finish Time (HEFT), Critical Path on a Processor (CPOP), and Predict Earliest Finish Time (PEFT) are considered for comparison. A non‐duplication version of LSTD named List Scheduling without Task Duplication algorithm is also considered for performance evaluation. The experimental analysis based on scientific Big Data workflows (CyberShake, Montage, and LIGO) proves that LSTD significantly surpasses all considered scheduling heuristics concerning schedule length ratio, the percentage of best results, and average running time metrics.