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Ordinary differential equations are fundamental tools for modeling dynamic systems in science, engineering, and applied mathematics. Solving these equations accurately and efficiently is crucial, particularly in cases where analytical solutions are challenging or impossible to obtain. This paper presents a method for solving inhomogeneous linear ordinary differential equations using an artificial neural network. The network is composed of a single input layer with one neuron, one hidden layer with three neurons, and a single output layer with one […]

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Artificial Intelligence of Things (AIoT) applications increasingly exceed the limits of centralized cloud processing because they require low latency, privacy preservation, scalability, and operational resilience. This review synthesizes distributed intelligence across the edge–fog–cloud continuum through a structured integrative methodology comprising multi-stage literature search, two-stage filtering, and thematic synthesis of more than 100 sources. The analysis covers four representative domains—industrial IoT, smart cities, connected healthcare, and smart agriculture—to identify recurring architectural patterns and shared deployment challenges. The review organizes […]

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Autism spectrum disorder (ASD) frequently manifests with profound language impairments, particularly in verb morphology processing, which hinges on fronto-temporal connectivity for grammatical rule application. This study pioneers the use of graph neural networks (GNNs) to map these deficits, analysing task-based fMRI data from 72 children (36 ASD, 36 controls). Fronto-temporal graphs were constructed with nodes representing key regions (e.g., inferior frontal gyrus, superior temporal gyrus) and edges capturing dynamic Pearson correlations during an auditory verb tense judgment task. […]

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Text classification in low-resource languages has become increasingly important due to the rapid growth of user-generated digital content. While multitask learning has long been studied in NLP, the use of LLMs for multitask text classification in low-resource languages such as Bengali remains underexplored. Although LLMs are inherently multilingual and multitasking, their effectiveness in structured multitask classification settings for Bengali has not been systematically evaluated. In this work, we investigate how LLMs can be leveraged for multitask Bengali text […]

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Accurate forecasting of recovery rates (RR) is central to credit risk management and regulatory capital determination. In many loan portfolios, however, RR modeling is constrained by data scarcity arising from infrequent default events. Transfer learning (TL) offers a promising avenue to mitigate this challenge by exploiting information from related but richer source domains, yet its effectiveness critically depends on the presence and strength of distributional shifts, and on potential heterogeneity between source and target feature spaces. This paper […]

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Waste-to-Energy Advances Using Domain-Specific AI Models and IoT for Scalable Biofuel Production 2026 introduces an innovative framework that leverages tailored artificial intelligence algorithms and Internet of Things infrastructure to transform heterogeneous organic waste streams into high-yield biofuels at industrial scales. By integrating graph neural networks for predictive modelling of biochemical reaction pathways and reinforcement learning for dynamic process optimization, the approach addresses longstanding inefficiencies in traditional waste-to-energy systems, such as variable feedstock quality and suboptimal reactor conditions. IoT-enabled […]

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The Maximum Independent Set (MIS) problem, a core NP-hard problem in graph theory, seeks the largest subset of vertices in an undirected graph $G = (V, E)$ with $n$ vertices and $m$ edges such that no two vertices are adjacent. We present a hybrid approximation algorithm that combines a vertex-cover complement approach with greedy selections based on minimum and maximum degrees, plus a low-degree induced subgraph heuristic, implemented using NetworkX. The algorithm preprocesses the graph to handle trivial […]

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The rapid proliferation of the Internet of Things (IoT) has positioned the Message Queuing Telemetry Transport (MQTT) protocol as a fundamental communication standard for large-scale, resource-constrained systems. Despite its lightweight design and scalability advantages, modern MQTT deployments operate under increasingly complex conditions characterized by intensive security enforcement, dynamic traffic patterns, and widespread use of wildcard subscriptions. These factors introduce tightly coupled challenges related to system performance, congestion, and security, which are often addressed independently in existing literature. This […]

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