Expert Systems最新文献

Accurate prediction of gastric cancer survival state is one of great significant tasks for clinical decision-making. Many advanced machine learning classification techniques have been applied to predict the survival status of cancer patients in three or 5 years, however, many of them have a low sensitivity because of class imbalance. This is a non-negligible problem due to the poor prognosis of gastric cancer patients. Furthermore, models in the medical domain require strong interpretability to increase their applicability. Due to the better performance and interpretability of the XGBoost model, we design a loss function taking into account cost sensitive and focal loss from the algorithm level for XGBoost to deal with the imbalance problem. We apply the improved model into the prediction of the survival status of gastric cancer patients and analyse the important related features. We use two types of indicators to evaluate the model, and we also design the confusion matrix of two models' predictive results to compare two models. The results show that the improved model has better performance. Furthermore, we calculate the importance of features related to survival with three different time periods and analyse their evolution, which are consistent with existing clinical research or further expand their research conclusions. These all support for clinically relevant decision-making and has the potential to expand into survival prediction of other cancer patients.

While machine learning's role in financial trading has advanced considerably, algorithmic transparency and explainability challenges still exist. This research enriches prior studies focused on high-frequency financial data prediction by introducing an explainable reinforcement learning model for portfolio management. This model transcends basic asset prediction, formulating concrete, actionable trading strategies. The methodology is applied in a custom trading environment mimicking the CAC-40 index's financial conditions, allowing the model to adapt dynamically to market changes based on iterative learning from historical data. Empirical findings reveal that the model outperforms an equally weighted portfolio in out-of-sample tests. The study offers a dual contribution: it elevates algorithmic planning while significantly boosting transparency and interpretability in financial machine learning. This approach tackles the enduring ‘black-box’ issue and provides a holistic, transparent framework for managing investment portfolios.

The threat of side-channel attacks poses a significant risk to the security of cryptographic algorithms. To counter this threat, we have designed an AES system capable of defending against such attacks, supporting AES-128, AES-192, and AES-256 encryption standards. In our system, the CPU oversees the AES hardware via the AHB bus and employs true random number generation to provide secure random inputs for computations. The hardware implementation of the AES S-box utilizes complex domain inversion techniques, while intermediate data is shielded using full-time masking. Furthermore, the system incorporates double-path error detection mechanisms to thwart fault propagation. Our results demonstrate that the system effectively conceals key power information, providing robust resistance against CPA attacks, and is capable of detecting injected faults, thereby mitigating fault-based attacks.

Facial emotion recognition (FER) represents a significant outcome of the rapid advancements in artificial intelligence (AI) technology. In today's digital era, the ability to decipher emotions from facial expressions has evolved into a fundamental mode of human interaction and communication. As a result, FER has penetrated diverse domains, including but not limited to medical diagnosis, customer feedback analysis, the automation of automobile driver systems, and the evaluation of student comprehension. Furthermore, it has matured into a captivating and dynamic research field, capturing the attention and curiosity of contemporary scholars and scientists. The primary objective of this paper is to provide an exhaustive review of FER systems. Its significance goes beyond offering a comprehensive resource; it also serves as a valuable guide for emerging researchers in the FER domain. Through a meticulous examination of existing FER systems and methodologies, this review equips them with essential insights and guidance for their future research pursuits. Moreover, this comprehensive review contributes to the expansion of their knowledge base, facilitating a profound understanding of this rapidly evolving field. In a world increasingly dependent on technology for communication and interaction, the study of FER holds a pivotal role in human-computer interaction (HCI). It not only provides valuable insights but also unlocks a multitude of possibilities for future innovations and applications. As we continue to integrate AI and facial emotion recognition into our daily lives, the importance of comprehending and enhancing FER systems becomes increasingly evident. This paper serves as a stepping stone for researchers, nurturing their involvement in this exciting and ever-evolving field.

This study addresses the imperative need for efficient solutions in the context of the dual resource constrained flexible job shop scheduling problem with sequence-dependent setup times (DRCFJS-SDSTs). We introduce a pioneering tri-objective mixed-integer linear mathematical model tailored to this complex challenge. Our model is designed to optimize the assignment of operations to candidate multi-skilled machines and operators, with the primary goals of minimizing operators' idleness cost and sequence-dependent setup time-related expenses. Additionally, it aims to mitigate total tardiness and earliness penalties while regulating maximum machine workload. Given the NP-hard nature of the proposed DRCFJS-SDST, we employ the epsilon constraint method to derive exact optimal solutions for small-scale problems. For larger instances, we develop a modified variant of the multi-objective invasive weed optimization (MOIWO) algorithm, enhanced by a fuzzy sorting algorithm for competitive exclusion. In the absence of established benchmarks in the literature, we validate our solutions against those generated by multi-objective particle swarm optimization (MOPSO) and non-dominated sorted genetic algorithm (NSGA-II). Through comparative analysis, we demonstrate the superior performance of MOIWO. Specifically, when compared with NSGA-II, MOIWO achieves success rates of 90.83% and shows similar performance in 4.17% of cases. Moreover, compared with MOPSO, MOIWO achieves success rates of 84.17% and exhibits similar performance in 9.17% of cases. These findings contribute significantly to the advancement of scheduling optimization methodologies.

The recent period has witnessed automated crawlers designed to automatically crack passwords, which greatly risks various aspects of our lives. To prevent passwords from being cracked, image verification codes have been implemented to accomplish the human–machine verification. It is important to note, however, that the most widely-used image verification codes, especially the visual reasoning Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs), are still susceptible to attacks by artificial intelligence. Taking the visual reasoning CAPTCHAs representing the image verification codes, this study introduces an enhanced approach for generating image verification codes and proposes an improved Convolutional Neural Network (CNN)-based recognition system. After we add a fully connected layer and briefly solve the edge of stability issue, the accuracy of the improved CNN model can smoothly approach 98.40% within 50 epochs on the image verification codes with four digits using a large initial learning rate of 0.01. Compared with the baseline model, it is approximately 37.82% better in accuracy without obvious curve oscillation. The improved CNN model can also smoothly reach the accuracy of 99.00% within 7500 epochs on the image verification codes with six characters, including digits, upper-case alphabets, lower-case alphabets, and symbols. A detailed comparison between our proposed approach and the baseline one is presented. The relationship between the time consumption and the length of the seeds is compared theoretically. Subsequently, we figure out the threat assignments on the visual reasoning CAPTCHAs with different lengths based on four machine learning models. Based on the threat assignments, the Kaplan-Meier (KM) curves are computed.

Video-based facial expression recognition (VFER) technique intends to categorize an input video into different kinds of emotions. It remains a challenging issue because of the gap between visual features and emotions, problems in handling the delicate movement of muscles, and restricted datasets. One of the effective solutions to solve this problem is the exploitation of efficient features defining facial expressions to carry out FER. Generally, the VFER find useful in several areas like unmanned driving, venue management, urban safety management, and senseless attendance. Recent advances in computer vision and deep learning (DL) techniques enable the design of automated VFER models. In this aspect, this study establishes a new Marine Predators Optimization with Deep Learning Model for Video-based Facial Expression Recognition (MPODL-VFER) technique. The presented MPODL-VFER technique mainly aims to classify different kinds of facial emotions in the video. To accomplish this, the presented MPODL-VFER technique derives features using the deep convolutional neural network based densely connected network (DenseNet) model. The presented MPODL-VFER technique employs MPO technique for the hyperparameter adjustment of the DenseNet model. Finally, Elman Neural Network (ENN) model is exploited for emotion recognition purposes. For assuring the enhanced recognition performance of the MPODL-VFER approach, a comparison study was developed on benchmark dataset. The comprehensive results have shown the significant outcome of MPODL-VFER model over other approaches.