The spread of plant diseases is influenced by a variety of environmental and pathogen factors that not only lower the production of fruits and grains but also cause quality deterioration. The timely detection and evaluation of diseases in food plants are crucial for the health of the agricultural industry and the country’s capacity to produce enough food. This research focused on prevalent illnesses in wheat and pea plants to explore the creation of an autonomous disease detection system designed for the unique traits of the examined crops, which can be adapted to the particularities of other crops. The experiments with artificial intelligence (AI) models included both traditional machine learning and sophisticated deep learning models, including customized models and transfer learning models. In this study, our framework employed the capabilities of Transformers, Random Forest, VGG16, a custom convolutional neural network (CNN) model, and two versions of You Only Look Once (YOLO), including both v5 and v8. The employability of classical machine learning techniques aims to gauge the computational feasibility of advanced techniques such as transformers and YOLO. This work also focused on the development of a standard dataset, which was developed by collecting healthy and diseased samples from various crop fields in Pakistan. The obtained results demonstrate the improved performance of the fine-tuned VGG16 model, transformers, and the customized version of CNN as compared to the Random Forest model and YOLOv8 algorithms, in terms of disease detection and classification accuracy. Moreover, the usability of YOLO versions and detection transformers for real-time disease identification has shown promising results, which is evident in its prospects for plant disease monitoring systems.
Background Named entity recognition (NER) is pivotal for medical information extraction and clinical decision support. However, most studies on Chinese medicine NER account for single-feature and multi-feature embeddings of Chinese characters, neglecting the multi-feature correlations inherent in Chinese characters. This limitation is exacerbated in rehabilitation medicine due to sparse annotated data, complex terminology, and significant character-level polysemy, which traditional models struggle to address effectively. Methods To bridge this gap, this article constructs a novel NER framework integrating a rehabilitation medicine chinese character knowledge graph (RMCCKG). The RMCCKG not only encompasses Chinese character features, including character, radical, pinyin, stroke, structure, part of speech, and morphology, but also embraces interrelationships between these features. Expanding on this foundation, we propose the RMCCKG+BERT-BiLSTM-CRF NER model. We employ RMCCKG embeddings and Bidirectional Encoder Representations from Transformers (BERT) embeddings as joint input representations, where the RMCCKG embeddings serve as prior knowledge to assist the model in better understanding the text content. This model enables character-level semantic enhancement This model enables character-level semantic enhancement through a hybrid architecture combining bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) layers. Results Experiments on our self-developed Rehab dataset and the public CMeEE dataset demonstrate that our model outperforms baseline methods, achieving a 3.96% F1-score improvement. Further, our studies further reveal that low-dimensional fusion of RMCCKG embeddings yields optimal performance, with significant gains in low-frequency entity recognition.
Muhammad Rehan Naeem, Rashid Amin, Muhammad Farhan
et al.
Collective intelligence systems like Chat Generative Pre-Trained Transformer (ChatGPT) have emerged. They have brought both promise and peril to cybersecurity and privacy protection. This study introduces novel approaches to harness the power of artificial intelligence (AI) and big data analytics to enhance security and privacy in this new era. Contributions could explore topics such as: leveraging natural language processing (NLP) in ChatGPT-like systems to strengthen information security; evaluating privacy-enhancing technologies to maximize data utility while minimizing personal data exposure; modeling human behavior and agency to build secure and ethical human-centric systems; applying machine learning to detect threats and vulnerabilities in a data-driven manner; using analytics to preserve privacy in large datasets while enabling value creation; crafting AI techniques that operate in a trustworthy and explainable manner. This article advances the state-of-the-art at the intersection of cybersecurity, privacy, human factors, ethics, and cutting-edge AI, providing impactful solutions to emerging challenges. Our research presents a revolutionary approach to malware detection that leverages deep learning (DL) based methodologies to automatically learn features from raw data. Our approach involves constructing a grayscale image from a malware file and extracting features to minimize its size. This process affords us the ability to discern patterns that might remain hidden from other techniques, enabling us to utilize convolutional neural networks (CNNs) to learn from these grayscale images and a stacking ensemble to classify malware. The goal is to model a highly complex nonlinear function with parameters that can be optimized to achieve superior performance. To test our approach, we ran it on over 6,414 malware variants and 2,050 benign files from the MalImg collection, resulting in an impressive 99.86 percent validation accuracy for malware detection. Furthermore, we conducted a classification experiment on 15 malware families and 13 tests with varying parameters to compare our model to other comparable research. Our model outperformed most of the similar research with detection accuracy ranging from 47.07% to 99.81% and a significant increase in detection performance. Our results demonstrate the efficacy of our approach, which unlocks the hidden patterns that underlie complex systems, advancing the frontiers of computational security.
The experience of an ACL2 user generally includes many failed proof attempts. A key to successful use of the ACL2 prover is the effective use of tools to debug those failures. We focus on changes made after ACL2 Version 8.5: the improved break-rewrite utility and the new utility, with-brr-data.
Zhengbing Hu, Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko
et al.
A fuzzy clustering algorithm for multidimensional data is proposed in this article. The data is described by vectors whose components are linguistic variables defined in an ordinal scale. The obtained results confirm the efficiency of the proposed approach.
I propose the purpose our concept of actual causation serves is minimizing various cost in intervention practice. Actual causation has three features: nonredundant sufficiency, continuity and abnormality; these features correspond to the minimization of exploitative cost, exploratory cost and risk cost in intervention practice. Incorporating these three features, a definition of actual causation is given. I test the definition in 66 causal cases from actual causation literature and show that this definition's application fit intuition better than some other causal modelling based definitions.
We study a framework where agents have to avoid aversive signals. The agents are given only partial information, in the form of features that are projections of task states. Additionally, the agents have to cope with non-determinism, defined as unpredictability on the way that actions are executed. The goal of each agent is to define its behavior based on feature-action pairs that reliably avoid aversive signals. We study a learning algorithm, called A-learning, that exhibits fixpoint convergence, where the belief of the allowed feature-action pairs eventually becomes fixed. A-learning is parameter-free and easy to implement.
We report about significant enhancements of the complex algebraic geometry theorem proving subsystem in GeoGebra for automated proofs in Euclidean geometry, concerning the extension of numerous GeoGebra tools with proof capabilities. As a result, a number of elementary theorems can be proven by using GeoGebra's intuitive user interface on various computer architectures including native Java and web based systems with JavaScript. We also provide a test suite for benchmarking our results with 200 test cases.
We introduce a kind of partial observability to the projective simulation (PS) learning method. It is done by adding a belief projection operator and an observability parameter to the original framework of the efficiency of the PS model. I provide theoretical formulations, network representations, and situated scenarios derived from the invasion toy problem as a starting point for some multi-agent PS models.
In the Markov decision process model, policies are usually evaluated by expected cumulative rewards. As this decision criterion is not always suitable, we propose in this paper an algorithm for computing a policy optimal for the quantile criterion. Both finite and infinite horizons are considered. Finally we experimentally evaluate our approach on random MDPs and on a data center control problem.
In this paper, we advocate the use of stratified logical theories for representing probabilistic models. We argue that such encodings can be more interpretable than those obtained in existing frameworks such as Markov logic networks. Among others, this allows for the use of domain experts to improve learned models by directly removing, adding, or modifying logical formulas.
In this paper a knowledge representation model are proposed, FP5, which combine the ideas from fuzzy sets and penta-valued logic. FP5 represents imprecise properties whose accomplished degree is undefined, contradictory or indeterminate for some objects. Basic operations of conjunction, disjunction and negation are introduced. Relations to other representation models like fuzzy sets, intuitionistic, paraconsistent and bipolar fuzzy sets are discussed.
We present a method for constructing the log-optimal portfolio using the well-calibrated forecasts of market values. Dawid's notion of calibration and the Blackwell approachability theorem are used for computing well-calibrated forecasts. We select a portfolio using this "artificial" probability distribution of market values. Our portfolio performs asymptotically at least as well as any stationary portfolio that redistributes the investment at each round using a continuous function of side information. Unlike in classical mathematical finance theory, no stochastic assumptions are made about market values.
We present ULSA, a novel stochastic local search algorithm for random binary constraint satisfaction problems (CSP). ULSA is many times faster than the prior state of the art on a widely-studied suite of random CSP benchmarks. Unlike the best previous methods for these benchmarks, ULSA is a simple unweighted method that does not require dynamic adaptation of weights or penalties. ULSA obtains new record best solutions satisfying 99 of 100 variables in the challenging frb100-40 benchmark instance.
The variability of structure in a finite Markov equivalence class of causally sufficient models represented by directed acyclic graphs has been fully characterized. Without causal sufficiency, an infinite semi-Markov equivalence class of models has only been characterized by the fact that each model in the equivalence class entails the same marginal statistical dependencies. In this paper, we study the variability of structure of causal models within a semi-Markov equivalence class and propose a systematic approach to construct models entailing any specific marginal statistical dependencies.
BPS, the Bayesian Problem Solver, applies probabilistic inference and decision-theoretic control to flexible, resource-constrained problem-solving. This paper focuses on the Bayesian inference mechanism in BPS, and contrasts it with those of traditional heuristic search techniques. By performing sound inference, BPS can outperform traditional techniques with significantly less computational effort. Empirical tests on the Eight Puzzle show that after only a few hundred node expansions, BPS makes better decisions than does the best existing algorithm after several million node expansions
Physical symbol systems are needed for open-ended cognition. A good way to understand physical symbol systems is by comparison of thought to chemistry. Both have systematicity, productivity and compositionality. The state of the art in cognitive architectures for open-ended cognition is critically assessed. I conclude that a cognitive architecture that evolves symbol structures in the brain is a promising candidate to explain open-ended cognition. Part 2 of the paper presents such a cognitive architecture.