Edge-cloud computing infrastructures are becoming a mandate in almost all the possible application areas due to exponential rise in the computational demands over the network. Therefore, adaptive load balancing and workload scheduling is critical in edge-cloud computing for maintaining Quality of Service (QoS) and system agility. To acquire an intelligent task scheduling and load distribution across heterogeneous computing environments, a novel AI-enabled predictive approach is presented in the proposed study for dynamic scheduling framework that integrates a Jacobi Identity-Based Deep Neural Network (JI-DNN) with a Multi-Criteria Grey Wolf Optimizer (MC-GWO). The proposed framework utilizes Jacobi identity properties for enhancing the network’s learning efficiency, stability, and convergence. The JI-DNN is employed for predicting task-specific execution times and heterogeneous resource requirements during run time task allocation in the system and the MC-GWO algorithm adapts the hunting behaviour of grey wolves for optimizing the scheduling decisions based on variant metrics like latency, energy usage, load balance, and overall outcome. The integration of predictive intelligence and multi-objective optimization helps the framework to adapt dynamically to real-time workload variations. Simulation result findings proves the proposed system’s effectiveness, highlighting the crucial improvements in reducing makespan, enhancing energy efficiency, and optimizing resource utilization as compared to the traditional static and single-criteria scheduling strategies. Clinical trial The study is not a clinical trial thus any registration details were not applicable.
Poultry is a major source of food, and growing demand for animal-based products has driven agricultural industries to increase production. However, this expansion has also led to a significant rise in the spread of infectious diseases. Several limitations faced by conventional methods include limited visual indicators, inability to capture complex interactions and limited integration of modern technology. The advancement of modern technology in the poultry industry helps to monitor and track the health of poultry chickens. The early detection of poultry diseases is essential for sustainable poultry farming, reducing poultry losses, and preventing the spread of zoonotic diseases to humans. In this work, a SpinalNet Fusion Recurrent Neural Network (SPFRNN) model is proposed for poultry disease classification based on Deep Learning (DL). The main objective of the proposed research is to design an enhanced deep learning-based poultry disease detection system that enables early and accurate classification of diseases, thereby reducing mortality rates, minimizing economic losses, and preventing the spread of infections. At first, the Internet of Things (IoT) is simulated, and images are collected from IoT nodes. Then, routing is performed at Base Station (BS) utilizing Proposed Jellyfish Search Honey Badger Optimization (JSHBO), whereas the optimal path is predicted by routing based on fitness parameters, such as energy, distance and delay. At BS, poultry disease is detected and classified. Initially, the input image is sent for pre-processing, which is performed by Anisotropic Filtering. Then, the disease area is segmented by Psi-Net, which is followed by Image augmentation. Moreover, suitable features, like Speeded Up Robust Features (SURF), Local binary pattern (CLBP), and Feature Local binary pattern (FLBP), along with statistical features are extracted in the feature extraction stage. Finally, poultry disease is detected by implementing a devised SPFRNN that integrates SpinalNet and Recurrent Neural Network (RNN). The proposed framework performs multi-level classification, where the first level identifies whether the poultry sample is healthy or diseased, and the second level classifies the detected diseased samples into specific disease categories, namely Coccidiosis, Salmonella, and Newcastle disease. Comparative evaluation demonstrates that the proposed SPFRNN model achieves superior performance, exhibiting improvements of 11.476
Forecasting financial markets remains difficult because price series are noisy, nonlinear, nonstationary, and subject to structural breaks. This structured review synthesizes empirical machine learning (ML) and deep learning (DL) research on equities, indices, commodities, foreign exchange, and cryptocurrencies, covering classical ML, recurrent networks, CNN-based models, attention and Transformer architectures, multimodal and graph-based systems, and reinforcement-learning approaches. Because studies differ in task formulation, horizon, data frequency, target variable, feature set, validation design, and metric choice, the review combines qualitative synthesis with structured quantitative evidence mapping rather than a single pooled effect size. The paired-error aggregation identified 17 peer-reviewed studies and 47 same-dataset, same-horizon proposed-versus-baseline error comparisons. On a study-level median basis, this subset showed a median relative error reduction of 20.3
Artificial intelligence (AI) tools such as chatbots have become an inseparable part of education with the advancements in large language models. As more studies have been conducted, researchers and practitioners may have challenges in finding the gaps in the literature and/or deciding on which tools are beneficial or not for instruction. These challenges can be addressed with bibliometric reviews that can provide a comprehensive overview of the current trends in AI use in ELT. This study addresses that need with a novel methodology by examining the ERIC and Scopus databases. Using computational data analysis tools and natural language processing, we examined 856 journal articles and provided several trends, such as keywords, the most cited papers, subject matrices, and geolocations of the articles, as well as several implications for practitioners. A key result is that the research on the use of AI tools is increasing substantially, with a clear focus on teaching methods and writing instruction. Our study offers valuable information for the researchers and practitioners who seek techniques to integrate AI tools into language instruction.
Accurate melanoma detection is essential for early-stage dermatological diagnosis, as it significantly improves treatment outcomes and patient survival rates. This study proposes a robust stacked ensemble framework that combines transfer learning (TL) with particle swarm optimization (PSO) to enhance melanoma classification performance. Initially, four deep transfer learning models: VGG16, ResNet50, DenseNet121, and InceptionV3 are trained on the Melanoma Skin Cancer Dataset consisting of 10,605 dermoscopic images to generate probability scores for melanoma and benign classes. These probability outputs are used to construct feature vectors for the subsequent stacked ensemble stage. The generated feature vectors are then provided as inputs to multiple meta-classifiers, whose hyperparameters are optimized using the PSO algorithm. PSO explores the hyperparameter search space iteratively, improving candidate solutions based on validation accuracy while utilizing both personal best and global best evaluations. Through this process, a total of eleven ensemble transfer learning (ETL) models are developed. Experimental results demonstrate that ETL11, which integrates VGG16, ResNet50, DenseNet121, and InceptionV3 with Gradient Boosting (GB), achieves the highest accuracy of 92.1
Remote sensing scene classification is essential in various Earth observation tasks. However, its accuracy often declines when images are acquired from different sensors or modalities due to domain shift. The differences in imaging mechanisms, sensor properties, and data distributions create significant discrepancies between the source and target domains, making cross-domain generalization challenging. Though domain adaptation methods have presented encouraging outcomes in reducing these drawbacks, the challenges of efficient feature separation, sensor heterogeneity, and distributional mismatch in multimodal datasets remain unresolved. To overcome these disadvantages, the present paper proposes a hierarchical and context-aware feature extraction framework based on the Swin Transformer for cross-domain remote sensing image classification. The proposed approach employs the Swin Transformer to capture both local and global spatial dependencies, enabling better domain-invariant feature representation across heterogeneous sensor modalities. The proposed framework is evaluated on the Multimodal Remote Sensing Scene Classification (MRSSC2.0) dataset across three difficult cross-domain transfer tasks: VIS→SWI, VIS→INF, and VIS→SAR. A thorough comparison among ten domain adaptation methods is conducted to determine the effectiveness of the proposed feature extraction approach. Experimental results show that the proposed framework is particularly effective when domain differences are high, such as in the VIS→SAR transfer. Further evaluation demonstrates the effectiveness of the proposed feature extraction framework when combined with existing domain adaptation methods, with FixBi achieving 96
Accurate leaf instance segmentation is fundamental to quantifying morphological and structural traits in image-based plant phenotyping. However, substantial variations in leaf scale, shape, and orientation, together with dense overlap and occlusion, often lead to ambiguous instance boundaries. In addition, repeated downsampling and feature reconstruction can progressively erode fine structural details, hindering contour preservation and the separation of adjacent leaves. To address these interrelated challenges, we propose DMSHA-Net, a dynamic multi-scale feature fusion and hierarchical attention network for leaf instance segmentation. Its direction-aware Multi-Scale Feature Aggregation (MSFA) encoder captures complementary horizontal and vertical contextual information across multiple receptive-field scales, thereby improving the representation of diverse leaf morphologies. The Dense Feature Aggregation (DFA) decoder selectively integrates deep semantic and shallow structural features through stage-specific attention mechanisms, where self-attention at coarse resolutions models long-range dependencies, whereas lightweight channel recalibration at high resolutions refines local structures and boundaries. The Learnable Feature Fusion (LFF) module subsequently integrates multi-level semantic and boundary features using normalized learned weights. DMSHA-Net achieves Best Dice (BD) scores of 93.17
Under the background of digital transformation of education, it has become a key issue to cultivate the core competence of digital teaching. However, the traditional digital teaching has limitations in situation creation, cognitive interaction and ability evaluation, and it is difficult to effectively support the cultivation of students’ higher-level ability. Multimodal Large Language Model (MLLM) provides a new opportunity to meet these challenges. Based on this, this study constructs a multimodal cognitive enhancement teaching model, which consists of three interrelated modules: multimodal situation generation, adaptive cognitive interaction and core competence evaluation. In this study, 120 college students participated in a 16-week quasi-experiment. The experimental group used the teaching platform based on MLLM, while the control group used the traditional online platform. The data were analyzed by statistical methods such as covariance analysis. The results show that the experimental group has significantly improved in the four core competence dimensions, and the adoption rate of innovative solutions has increased by 32.85
A large part of the cost of producing Digital to Analog Converters (DACs) is related to testing, due to factors such as long time taken for analog fault diagnosis, increased testing time and expensive equipment being required for testing. Therefore, using Machine Learning (ML) based fault classification offers a promising alternative method for testing DACs compared to traditional testing, as it reduces complexity while also improving fault diagnostic accuracy. This study compares the performance of Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Probabilistic Neural Network (PNN) to classify catastrophic faults in both 16-bit Charge Scaling and Binary Weighted DACs that have experienced process variation such as variable threshold voltage, oxide thickness and temperature. Additionally, this paper proposes a framework to eliminate the need for additional test hardware, as the simulated fault signatures are used to develop and validate classifiers for both Charge Scaling and Binary Weighted DACs. The experimental results indicate that the BPNN classified faults with an average classification accuracy of 100
Early and accurate discovery of brain tumours is crucial for timely diagnosis and effective treatment planning. In this work, we put forward an explainable multimodal CNN–Transformer framework, kind of merges MRI and CT cues in a complementary way, using attention-guided multimodal fusion so that tumour classification and localisation can happen at the same time. Instead of most existing multimodal CNN–Transformer methods, which mostly linger on feature merging for classification only, this proposal does a bit more. It uses modality-specific CNN encoders, a Transformer based cross-modal attention module, and several fusion recipes—early fusion, late fusion, and attention-driven fusion. On top of that we do quantitative Grad-CAM checking against expert radiologist annotations, plus a computational efficiency examination, all inside a single coherent architecture. Overall this should boost interpretability and make the approach more clinically usable. For training and evaluation, the framework was tested with a carefully curated dataset of 1,986 patient-level MRI–CT pairs collected from BraTS 2021 and the TCIA Brain Tumour CT Archive. The dataset includes glioma, meningioma, pituitary tumour, and healthy controls. We used a patient-wise split: 70
Vehicular Ad Hoc Networks (VANETs) are a major enabling technology for Intelligent Transportation Systems (ITS), facilitating safety-critical, traffic-efficient vehicle-to-vehicle (V2V) communication. Nevertheless, due to the highly dynamic nature of the vehicular environment, heterogeneous mobility patterns that ensure stable clustering and consistent cluster head (CH) selection pose a challenge. Existing embedding-driven and graph-based vehicle clustering methods rely on localized, one-hop neighbourhood aggregation and assume homogeneous neighbour influence. To overcome these challenges, the paper proposes an attention-guided deep embedding framework that jointly models long-range structural interactions and heterogeneous vehicular influence, rather than restricting learning to immediate neighbours. The proposed algorithm trains latent vehicular representations that understand long-range structural interactions that go beyond immediate one-hop neighbourhoods using Deep Nonnegative Matrix Factorisation (DANMF). To capture the heterogeneous vehicle influence (varying mobility, distance over time of vehicles), the dominant connectivity patterns are highlighted by using an ensemble attention mechanism. This incorporates interaction-factor attention to highlight the high-weighted interaction patterns of latent representation, whereas, through vehicle-level proximity attention, the stable and behaviourally related neighbours are selectively highlighted. Clustering is done according to the learned embeddings, and cluster heads are selected based on the strongest affinity of vehicles to cluster centres, to guarantee that CHs represent varying influence of mobility and structural significance. Extensive simulations reveal that the proposed model outperforms baselines, with a maximum throughput of 7.1 Mbps , CH stability of 90 ms , which translates to 36
The online retail platforms of the last decade have gone through an exponential expansion, with Amazon being a prime example. This expansion has resulted in the generation of a vast amount of user–product interaction data, which traditional single-node systems struggle to process effectively. This research presents a scalable, deployment-oriented e-commerce recommendation system that uses Apache Spark, Hadoop, Pig, Hive, and Mahout within a Dockerized big data ecosystem. The pipeline integrates collaborative filtering using Alternating Least Squares (ALS) and content-based filtering using TF–IDF into a hybrid recommendation engine, and is automated end-to-end for ingestion, preprocessing, modeling, and evaluation, with a Flask web interface for monitoring and configuration. The system is evaluated on Amazon review datasets ranging from a 4.6-million-rating modeling subset to a full ingestion corpus exceeding 40 million records. On a real three-node Apache Spark/Hadoop cluster (HDFS replication factor 3), we empirically demonstrate sub-linear growth of training time with data volume, a measurable multi-node speedup, and fault tolerance under a deliberately induced node failure. The hybrid model improves recommendation quality over the standalone models, attaining an RMSE as low as ≈ 0.94 and Precision@10 up to ≈0.74 across the evaluation datasets. This research provides a comprehensive account of the design, implementation, evaluation, and future directions for scalable big data analytics in recommendation systems.
Fog–edge Internet of Things (IoT) systems support low-latency distributed services but remain vulnerable to coordinated attacks that evade detectors designed for independent events. Existing solutions also tend to separate attack detection, provenance verification, and accountability enforcement, leaving no unified path from relational evidence to auditable response. This study aims to develop an integrated framework that detects coordinated malicious behavior, ranks provenance relevance, verifies evidence, and activates rule-based accountability in resource-constrained edge environments. The proposed Blockchain-Fog Computing Collaborative Framework with Deep Attention-based Collusion Detection and Automated Accountability (BF3-ACDA) framework combines a hierarchical blockchain–fog architecture with an attention-based collusion graph neural network (AttnCol-GNN), whose reputation-aware attention coefficient incorporates behavioral correlation and blockchain-derived trust information. A shared attention representation supports both collusion classification and provenance ranking, while Fog-BFT consensus, Merkle verification, and smart contracts provide tamper-evident recording and severity-based enforcement. Across 30 matched independent runs on the collusion-augmented CICIoT2023 benchmark, BF3-ACDA achieved 96.80 ± 0.23
This longitudinal qualitative multiple-case study examined how 24 undergraduate EFL students at a Jordanian public university experienced generative AI during a 12-week academic writing course. The multi-source dataset included successive drafts, archived ChatGPT interactions, decision logs, reflective journals, interviews, and stimulated-recall sessions. Reflexive thematic analysis was combined with episode-level tracing. Impostor phenomenon was used as a sensitizing lens rather than as a measured participant attribute. The findings showed that AI reduced immediate drafting and language difficulties but sometimes shifted uncertainty toward ownership, legitimacy, deservingness, and fear of exposure. Students did not treat AI as a consistently reliable authority; instead, they accepted, adapted, resisted, and, most frequently, verified its suggestions. Concerns about hidden expectations and imagined judgment were evident before a Kafka-focused mini-unit. After the unit, some students used terms such as hidden rules and invisible judges to express these concerns more clearly. The findings therefore suggest that Kafka provided a shared language for reflection rather than creating these concerns or causing changes in authorship. Overall, structured AI use supported more accountable textual decision-making while also generating new affective and authorship-related pressures.
Deep Convolutional Neural Networks (CNNs) have gained immense popularity over the past decade due to their exceptional performance, especially in imaging applications. However, the primary focus has been on improving model accuracy, often overlooking the significant environmental and computational costs associated with it. Pruning, a model compression technique, has been popularly used to reduce model size and computational complexity. Despite rapid growth of interest in this topic, research which comprehensively studies the energy consumption and targets energy efficiency using structured pruning is to date still missing. In this paper, we propose a simple, novel and energy-efficient structured pruning methodology resulting in energy, memory footprint and parameter reduction. Using the above methodology, an energy reduction of ranging from 6.49 ∼ 1% ) in accuracy. The one-shot version of the energy-aware pruning algorithm achieves an impressive 74.64
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to challenges in early-stage detection and accurate risk stratification. Conventional machine learning models often exhibit limited interpretability and reduced predictive capability when modelling complex, non-linear relationships among demographic and lifestyle risk factors. Therefore, robust, explainable, and methodologically rigorous predictive frameworks are required to support reliable clinical decision-making. This study proposes a systematically validated explainable ensemble learning framework for lung cancer stage prediction by integrating optimized stacking and voting strategies. A leakage-free machine learning pipeline comprising data preprocessing, normalization, recursive feature elimination (RFE), and GridSearchCV-based hyperparameter optimization was developed. Multiple machine learning classifiers were combined through optimized ensemble learning, while SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were employed to provide complementary global and local interpretability. Model performance was evaluated using an independent hold-out test set, stratified 10-fold cross-validation, 95
Preparing and organising a relief effort using useful social media content in a crisis situation is crucial. Twitter, Facebook, LinkedIn, and many more social media platform produces immense data. Practical implementation for necessary automatic sorting and extracting specific text by classifying tweets into useful information, which helps humanitarian needs. Demanding less computational resources to concentrate on text models to scale the work from the CrisisMMD dataset. Machine Learning (ML) classifier test for converting text by using Term Frequency Inverse Document Frequency (TF-IDF), Word2Vec, and Bag of Words (BoW). Performance measure is used to evaluate accuracy, macro-averaged precision, recall, F1-score, and ROC-AUC, and tested for statistical significance and ROC curves analysis, with standard metrics on the classifiers Logistic Regression (LR), Support Vector Machines (SVM), Multinomial Naïve Bayes (MNB), Decision Trees (DT), Random Forests (RF), XGBoost, and a combined ensemble model. Better results were obtained with the Word2Vec model than with TF-IDF, and BoW consistently delivers effective word-count-based methods, indicating simpler methods. Logistic Regression (LR) and SVMs achieve consistent and reliable results in linear models when used in an ensemble approach with measured criteria yielding the most well-rounded performance. To identify humanitarian needs and class distribution, and to correct the models to strengthen especially critical disaster response. The research focuses on demonstrating traditional ML techniques, paired with reliable test filtering and the right extraction features. Disaster-related test filtering to present real-time monitoring supports humanitarian decision-making.
The field of swarm robotics has become more and more popular as a decentralised solution to problems of coordination among multiple independent agents. The recent developments in multi-agent reinforcement learning (MARL) have made it possible for agents to learn cooperative behaviours when operating in a dynamic environment and even outperform the traditional rule-based or heuristic coordination strategies. However, coordination in practical MARL-based swarms is still difficult, as many approaches are not scalable, have high communication cost, unstable coordination with high swarm density, and lack integration of efficiency, robustness, and adaptability. This paper introduces a multi-agent reinforcement learning framework for scalable and communication-efficient swarm coordination called RoboSwarmCoordAI, which surpasses the limitations of the above approaches. The proposed framework uses three major components: a state-encoding module that is aware of the coordination requirements, an adaptive neighbourhood-filtering module to avoid redundant inter-agent communication and a hybrid reward function that weights local robot goals with respect to the global swarm performance. RoboSwarmCoordAI uses a centralised training and decentralised execution approach where agents can leverage global information for training, but local information for execution. The framework was tested in simulation in cooperative exploration, distributed target search and task allocation scenarios. When evaluated within the range of simulations tested, RoboSwarmCoordAI outperformed baseline methods with a task success rate of 95.8
Medical image segmentation is essential for computer-aided diagnosis and treatment planning. Privacy constraints impede centralized training using medical images from diverse healthcare organizations. Federated learning (FL) is developed to facilitate collaborative model training without sharing patient information. However, the statistical heterogeneity of data (non-IID distribution) among clients adversely impacts the segmentation task, particularly the quality of segmentation at object borders, which remains insufficiently explored. This paper presents a systematic analysis of boundary-sensitive medical image segmentation under heterogeneous federated learning conditions. We propose FedBound, a lightweight boundary-aware optimization technique that emphasizes contour areas during local training without increasing communication overhead. Additionally, we examine the impact of multiscale feature representations using an ASPP-based federated framework termed FedASPP. Experiments were conducted on the ISIC 2018 dataset for skin lesion segmentation, employing a Dirichlet non-IID distribution across 100 federated clients. The findings indicate that FedBound enhances boundary quality, reduces the HD95 score, and maintains high Dice and IoU coefficients across various segmentation architectures. Furthermore, FedBound reduces performance variability among clients, demonstrating improved stability in heterogeneous federated environments.
Rising automobile populations have led to increased accident rates, resulting in significant fatalities and property damage worldwide. The collision avoidance system (CAS) is one of the significant components in vehicle safety enhancement. The integration of industrial internet of things (IIoT) with CAS, the advantages of this technology, and its drawbacks have all been examined in this study. Furthermore, in order to enhance the IIoT-based CAS for automobiles, analytical techniques, algorithms, and protocols have been suggested. The results show that IIoT-based CAS can improve vehicle safety through accident reduction and faster response times. Also, the number of IIoT-based CAS (i.e., capacity analysis) that can be accommodated using different generations of WLAN standards like IEEE 802.11b/g/n/ac/ax/be has been obtained. The simulation and experimental results show that the IIoT-enabled CAS can achieve a 13.26
