The proposed ACL-PEFT-LLM model combines Adaptive Curriculum Learning (ACL) and Parameters-Efficient Fine-tuning (PEFT) to overcome the shortcomings of current models that employ full fine-tuning, which is computationally expensive and impractical in many domain-specific applications. Although PEFT methods such as LoRA, Prefix Tuning, and Prompt Tuning have been designed to optimize only a few model parameters, thereby reducing memory usage and training time by a significant factor, typically lack an adaptive mechanism to adjust task difficulty during training. By comparison, ACL-PEFT-LLM dynamically adjusts the difficulty of training examples based on the model's current performance, enabling it to start with easier tasks and increase in difficulty. This will guarantee that the model learns effectively without being flooded with challenging examples during the initial stages of learning. The ACL-PEFT-LLM model is superior to the other models in both accuracy and computational efficiency. It has an F1 score of 96.2 and a high accuracy of 96.8, indicating strong task-specific performance across a wide range of datasets, including SST-2, SQuAD, and AIME. Besides, it has a high Accuracy-Efficiency Ratio (AER) of 5.12, indicating a positive trade-off between performance and resource consumption. Compared with other methods, LoRA is most efficient in terms of training time and GPU memory usage, with an accuracy of 94.0 and an F1 score of 93.7, but is marginally lower in performance. Other techniques, such as Full Fine-Tuning, Prefix Tuning, and Adapters, are either less accurate or more resource-intensive, whereas ACL-PEFT-LLM is the most efficient mechanism for domain-specific fine-tuning. ACL-PEFT-LLM offers a potent platform that facilitates efficient domain adaptation at minimal resource usage, which makes it suitable for tasks in domains such as medical, legal, and financial, where computing resources are usually scarce. This model has found a very good compromise between maximum performance and computational efficiency such that tasks that belong to a domain can be efficiently performed without overuse of resources.
Cybercriminals have become increasingly interested in the spread of critical information, particularly in interpersonal contact and mass distribution of programs and file downloads. This has heightened researchers' awareness of the rampant spread of malware and data breaches. It is anticipated that the number and intensity of malicious software will continue to rise, underscoring the imperative need for strong security architectures. This is especially critical for mobile networks and ubiquitous computing security, given the growing threat posed by hackers. The proposed project involves creating a new dataset using a strictly controlled, shared sample pool to address security threats in wireless mobile networks, leveraging dynamic analysis techniques for malware detection. The dataset aims to enhance the recognition of malicious software by leveraging methods such as encryption and obfuscation. The suggested classification algorithm is CNN-LSTM, a combination of Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) model, which excels at learning complex, sequential features. The CNN and LSTM models were tested on a dataset comprising more than 10,000 malware samples and achieved accuracies of 98% and 97%, respectively. These findings demonstrate how deep learning models can be used to enhance the security of mobile networks and provide effective protection against emerging threats in mobile and ubiquitous computing systems, in a highly beneficial way.
Breast cancer is one of the most common and deadliest diseases that women globally experience. An early and accurate diagnosis may improve the chance of treatment success and increased survival rate. Current traditional diagnostic techniques used for breast cancer like mammography, ultrasound, and MRI are largely annotated by experts, creates variability and inconsistency in the diagnosis. New developments in artificial intelligence (AI) and deep learning could offer promising solutions for touchless high-precision breast cancer diagnosis. This study proposes an Advanced Multi-View Convolutional-Recurrent Network (AMVCRN), a unique design that integrates Convolutional Neural Networks used for extracting spatial features, with Recurrent Neural Networks, using Gated Recurrent Units – GRUs, for temporal sequence modeling. The hybrid model allows greater analysis of tumor features from multi-view images collected from different modalities and angles. The intended outcomes are to improve classification accuracy and reduce errors in persecution. Ultimately, it is intended to yield and sound a decision support system for clinical radiologists.
Today, malicious actors often use social media to send, receive, and post false, misleading, or offensive content about other people. The effects of social media bullying on its targets are similar to those of threats, gossip, and harassment in the physical world. An alarming rise in mental health issues has resulted from cyberbullying, particularly in the younger population. The effects can include suicidal thoughts and low self-esteem. Several conventional machine learning techniques have been employed to automatically identify cyberbullying on social media. Although some traditional methods of machine learning have been designed to automatically identify cyberbullying, most of them are characterized by weaknesses in feature selection and classification rates. In order to deal with such issues, a new Termite Apriori Prediction Framework (TAPF) has been proposed in this paper. The model consists of a data collection and preprocessing phase to remove noise and a hybrid feature selection phase that combines Apriori rule mining with termite optimisation to select the most discriminative features. Cyberbullying is then classified with the help of these optimized features. It has been experimentally shown that TAPF performs better than the classical models such as the Logistic Regression, Naive Bayes, Long Short-Term Memory (LSTM), and the Support Vector Machine (SVM). The proposed structure has a recall rate of 96.2, precision of 96.7, F-score of 96.3, and an overall accuracy of 96.4, which is over 2 per cent higher than the best-performing baseline (SVM). Applied to a Python environment, TAPF offers an effective and dependable mechanism of detecting cyberbullying on social media sites.
The success and the importance of industrial robotic arms in contemporary automation rely on their accuracy, velocity, and capability of functioning under varied conditions, but mechanical and electromechanical deteriorations, including bearing wear, gear backlash, joint misalignment, and imbalance of motor torque, may negatively affect performance, safety, and production unless inner problems are revealed early. The traditional methods of maintenance, such as reactive repairs and fixed-interval inspections, are inefficient and do not capture the faults in their early stages, whereas the current machine learning-based solutions typically process sensor signals separately, neglecting inter-joint kinematic constraints, and are not physically interpretable, leading to poor performance in cases of rare or hidden faults. This paper will solve these challenges by introducing a Hierarchical Temporal-Graph Physics-Guided Fusion Network (HT-GP-FusionNet) in predictive fault diagnosis of robotic arms. The framework incorporates hierarchical temporal modeling to decompose short-term and long-term dynamics in multi-sensor data consisting of accelerometers, gyroscopes, motor currents, and joint positions, and a graph neural network fully captures the inter-joint relationships and fault propagation along the kinematic chain. It uses a physics-based regularization that requires consistency with motion equations and energy conservation laws, and generates samples of faults to be generated by a generative fault augmentation model to promote few-shot generalization. The experimental results on a set of 50,000 sequences (10 sensors, five fault types) sampled at 1 kHz show that HT-GP-FusionNet outperforms CNN (89.3), LSTM (90.5), CNN-LSTM (92.8), and GNN-based (93.6) models in all metrics and has higher recall with rare faults, and can be used when the number is unbalanced. These conclusions have been proved by ablation experiments that show temporal modeling, graph reasoning, physics-guided regularization, and data augmentation make significant contributions to performance. In general, HT-GP-FusionNet offers a scalable, interpretable, and physics-consistent solution to the early and accurate predictive maintenance, hence higher reliability, safety, and operational efficiency during industrial robotic systems.
