Cancer’s staggering molecular heterogeneity demands innovative approaches beyond traditional single-omics methods. The integration of multi-omics data, spanning genomics, transcriptomics, proteomics, metabolomics and radiomics, can improve diagnostic and prognostic accuracy when accompanied by rigorous preprocessing and external validation; for example, recent integrated classifiers report AUCs around 0.81–0.87 for difficult early-detection tasks. This review synthesizes how artificial intelligence (AI), particularly deep learning and machine learning, bridges this gap by enabling scalable, non-linear integration of disparate omics layers into clinically actionable insights. We explore cutting-edge AI methodologies, including graph neural networks for biological network modeling, transformers for cross-modal fusion, and explainable AI (XAI) for transparent clinical decision support. Critical applications are highlighted, such as AI-driven therapy selection (e.g., predicting targeted therapy resistance), proteogenomic early detection, and radiogenomic non-invasive diagnostics. We further address translational challenges: data harmonization, batch correction, missing data imputation, and computational scalability. Emerging trends, federated learning for privacy-preserving collaboration, spatial/single-cell omics for microenvironment decoding, quantum computing, and patient-centric “N-of-1” models, signal a paradigm shift toward dynamic, personalized cancer management. Despite persistent hurdles in model generalizability, ethical equity, and regulatory alignment, AI-powered multi-omics integration promises to transform precision oncology from reactive population-based approaches to proactive, individualized care.

Fog computing has emerged as a computing paradigm for resource-restricted Internet of things (IoT) devices to support time-sensitive and computationally intensive applications. Offloading can be utilized to transfer resource-intensive tasks from resource-limited end devices to a resource-rich fog or cloud layer to reduce end-to-end latency and enhance the performance of the system. However, this advantage is still challenging to achieve in systems with a high request rate because it leads to long queues of tasks in fog nodes and reveals inefficiencies in terms of delays. In this regard, reinforcement learning (RL) is a well-known method for addressing such decision-making issues. However, in large-scale wireless networks, both action and state spaces are complex and extremely extensive. Consequently, reinforcement learning techniques may not be able to identify an efficient strategy within an acceptable time frame. Hence, deep reinforcement learning (DRL) was developed to integrate RL and deep learning (DL) to address this problem. This paper presents a systematic and comprehensive review of recently published studies that have utilized RL or DRL algorithms to address offloading-related issues in fog computing. First, the taxonomy of fog computing offloading mechanisms based on RL and DRL algorithms was divided into three major categories: value-based, policy-based, and hybrid-based algorithms. These categories were then compared based on important features, including offloading problem formulation, utilized techniques, performance metrics, evaluation tools, case studies, their strengths and drawbacks, offloading directions, offloading mode, SDN-based architecture, and offloading decisions. Finally, we discuss the future research directions and open issues.

Flooding is one of the most prevalent types of natural catastrophes, and it can cause extensive damage to infrastructure and the natural environment. The primary method of flood risk management is flood susceptibility mapping (FSM), which provides a quantitative assessment of a region’s vulnerability to flooding. The objective of this study is to develop new ensemble models for FSM by integrating metaheuristic algorithms, such as genetic algorithms (GA), particle swarm optimization (PSO), and harmony search (HS), with the decision table classifier (DTB). The proposed algorithms were applied in the province of Sulaymaniyah, Iraq. Sentinel-1 synthetic aperture radar (SAR) data satellite images were used for flood monitoring (on 27 July 2019), and 160 flood occurrence locations were prepared for modeling. For the training and validation datasets, flood occurrence data were coupled to 1 flood-influencing parameters (slope, altitude, aspect, plan curvature, distance from rivers, land cover, geology, topographic wetness index (TWI), stream power index (SPI), rainfall, and normalized difference vegetation index (NDVI)). The certainty factor (CF) approach was used to determine the spatial association between the effective parameters and the occurrence of floods, and the resulting weights were employed as modeling inputs. According to the pairwise consistency technique, the NDVI and altitude are the most significant factors in flood modeling. The area under the receiver operating characteristic (AUROC) curve was used to evaluate the accuracy and effectiveness of ensemble models. The DTB-GA model was found to be the most accurate (AUC = 0.889), followed by the DTB-PSO model (AUC = 0.844) and the DTB-HS model (AUC = 0.812). This research’s hybrid models provide a reliable estimate of flood risk, and the risk maps are reliable for flood early-warning and control systems.

Fog computing (FC) has recently emerged as a promising new paradigm that provides resource-intensive Internet of Things (IoT) applications with low-latency services at the network edge. However, the limited capacity of computing resources in fog colonies pose great challenges for scheduling and allocating application tasks. In this paper, we propose an intelligent scheduling strategy algorithm in a FC system based on multi-Objective Deep Reinforcement learning (MODRL) to select nodes for task processing (fog nodes or cloud) based on three objectives: node current load, node distance, and task priority. The proposed model addresses two main problems: task allocation and task scheduling. We employ three Deep Reinforcement Learning (DRL) agents based on deep Q network (DQN), one for each objective. However, this is a more challenging scenario because there is a trade-off among these objectives, and eventually, each algorithm may select different processing nodes according to its own objective, which brings us to a Pareto Front problem. To solve this problem, we propose using a multi-objective optimization; multi-objective evolutionary algorithm based on decomposition (MOEA/D), and non-dominated sorting genetic algorithm (NSGA2), which are multi-objective optimization algorithm that can choose the optimal node while considering three objectives. The simulation results show that our proposed intelligent scheduling strategy could achieve better outcomes for the various employed performance, efficiency, and adaptability metrics: task completion time, makespan, queueing delay, propagation delay, transmission delay, processing delay, computational delay, latency, CPU load, and storage utilization, with an average value of 2.02ms, 10ms,2ms, 9.9ms, 25ms, 1.0ms, 3.5ms, %10, and % 99, respectively, compared with the existing related research studies.

This paper presents a novel offloading technique designed to enhance the efficiency of Internet of Things (IoT) applications within a sophisticated three-layer architecture situated in a fog computing environment. The IoT layer contains various intelligent IoT devices that generate a large number of tasks, each characterized by distinct specifications such as size, computational demand, communication requirements, and latency constraints. owing to the limited storage and computing capacity of resource-constrained IoT devices, it is essential to offload these tasks to different layers to ensure effective processing while satisfying the required Quality of Service (QoS) goals. To address this challenge, a fuzzy logic-based task scheduler is employed to make informed offloading decisions, considering task attributes and determining the most suitable processing layers—whether locally at the IoT layer, on collaborative fog nodes, or in the cloud. Furthermore, the study leverages the Deep Q Network (DQN) method, a form of deep reinforcement learning, to identify the optimal fog node for offloading tasks and to maintain a balanced workload distribution across collaborative fog nodes. The experimental findings demonstrate that the proposed scheme outperforms state-of-the-art solutions in terms of latency, power consumption, network usage, throughput, and offloading rate in comparison with the Non-offload, First-Fit, GASDEO, and NAFITO-FLA methods.

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.