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| viXra.org > Artificial Intelligence > 2310 |
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[7] viXra:2310.0150 [pdf] submitted on 2023-10-30 04:27:45
Authors: Donggyu Lee
Comments: 14 Pages.
This study presents an active memory algorithm that generates responses in generative language models using graph databases. The development of generative language models has picked up pace recently, and there are many commercial services available. However, generative language models are limited by problems such as hallucination, low accuracy and reliability, and limitations in contextualizing and remembering. It is expensive and requires a lot of resources to develop pre-training datasets or fine-tune the base model to address these problems. Instead, well-designed prompts can be used to achieve the desired response, but this requires prompt engineers or training, as well as a thorough understanding of generative language models.All conversations are saved in a graph database to build a memory, and when a user asks a question, it proactively identifies the information it needs and pulls it and its neighbors from the graph database for reference as it generates an answer to the question. This approach streamlines the generation of natural language that disentangles complex and interconnected information in the real world. Research has shown that answering questions based on real-world information increases the efficiency and usability of generative language models in processing information and generating answers.In addition, the memory assist algorithm of the graph database converts various text datasets, not only conversations, into property graph models that can be updated in real time, and provides diverse and accurate information to the generative language model, enabling it to generate accurate responses through diverse information while reducing the size of the language model, thereby increasing efficiency and speed.
Category: Artificial Intelligence
[6] viXra:2310.0118 [pdf] submitted on 2023-10-24 02:48:10
Authors: Zenin Easa Panthakkalakath, Juraj Kardoš, Olaf Schenk
Comments: 11 Pages.
The boundary control problem is a non-convex optimization and control problem in many scientific domains, including fluid mechanics, structural engineering, and heat transfer optimization. The aim is to find the optimal values for the domain boundaries such that the enclosed domain adhering to the governing equations attains the desired state values. Traditionally, non-linear optimization methods, such as the Interior-Point method (IPM), are used to solve such problems.This project explores the possibilities of using deep learning and reinforcement learning to solve boundary control problems. We adhere to the framework of iterative optimization strategies, employing a spatial neural network to construct well-informed initial guesses, and a spatio-temporal neural network learns the iterative optimization algorithm using policy gradients. Synthetic data, generated from the problems formulated in the literature, is used for training, testing and validation. The numerical experiments indicate that the proposed method can rival the speed and accuracy of existing solvers. In our preliminary results, the network attains costs lower than IPOPT, a state-of-the-art non-linear IPM, in 51% cases. The overall number of floating point operations in the proposed method is similar to that of IPOPT. Additionally, the informed initial guess method and the learned momentum-like behaviour in the optimizer method are incorporated to avoid convergence to local minima.
Category: Artificial Intelligence
[5] viXra:2310.0096 [pdf] submitted on 2023-10-21 03:56:45
Authors: Sudhanshu Sekhar Tripathy, Bichitrananda Behera
Comments: 20 Pages. Please Publish My preprint article
The escalation of hazards to safety and hijacking of digital networks are among the strongest perilous difficulties that must be addressed in the present day. Numerous safety procedures were set up to track and recognize any illicit activity on the network's infrastructure. IDS are the best way to resist and recognize intrusions on internet connections and digital technologies. To classify network traffic as normal or anomalous, Machine Learning (ML) classifiers are increasingly utilized. An IDS with machine learning increases the accuracy with which security attacks are detected. This paper focuses on intrusion detection systems (IDSs) analysis using ML techniques. IDSs utilizing ML techniques are efficient and precise at identifying network assaults. In data with large dimensional spaces, however, the efficacy of these systems degrades. Correspondingly, the case is essential to execute a feasible feature removal technique capable of getting rid of characteristics that have little effect on the classification process. In this paper, we analyze the KDD CUP-'99' intrusion detection dataset used for training and validating ML models. Then, we implement ML classifiers such as "Logistic Regression, Decision Tree, K- Nearest Neighbour, Naïve Bayes, Bernoulli Naïve Bayes, Multinomial Naïve Bayes, XG-Boost Classifier, Ada- Boost, Random Forest, SVM, Rocchio classifier, Ridge, Passive-Aggressive classifier, ANN besides Perceptron (PPN), the optimal classifiers are determined by comparing the results of Stochastic Gradient Descent and back- propagation neural networks for IDS", Conventional categorization indicators, such as "accuracy, precision, recall, and the f1-measure", have been used to evaluate the performance of the ML classification algorithms.
Category: Artificial Intelligence
[4] viXra:2310.0061 [pdf] replaced on 2024-07-18 02:28:48
Authors: Mohammadjavad Maheronnaghsh, Mohammad Mahdi Gheidi, Abolfazl Younesi, MohammadAmin Fazli
Comments: 11 Pages. I have uploaded another versions before. Please remove the previous versions from ViXra.
In the dynamic world of financial markets, accurate price predictions are essential forinformed decision-making. This research proposal outlines a comprehensive study aimed at forecasting stock and currency prices using state-of-the-art Machine Learning (ML) techniques. By delving into the intricacies of models such as Transformers, LSTM, Simple RNN, NHits, and NBeats, we seek to contribute to the realm of financial forecasting, offering valuable insights for investors, financial analysts, and researchers. This article provides an in-depth overview of our methodology, data collection process, model implementations, evaluation metrics, and potential applications of our research findings.The research indicates that NBeats and NHits models exhibit superior performance in financial forecasting tasks, especially with limited data, while Transformers require more data to reach full potential. Our findings offer insights into the strengths of different ML techniques for financial prediction, highlighting specialized models like NBeats and NHits as top performers - thus informing model selection for real-world applications.
Category: Artificial Intelligence
[3] viXra:2310.0047 [pdf] submitted on 2023-10-10 21:49:36
Authors: Budee U. Zaman
Comments: 5 Pages.
The integration of Artificial Intelligence (AI) into education has the potential to revolutionize traditional teaching and learning methods. AI can offer personalized learning experiences, streamline administrative tasks,enhance feedback mechanisms, and provide robust data analysis. Numerous studies have demonstrated the positive impact of AI on both student outcomes and teacher efficiency. However, caution must be exercised when implementing AI in education, considering potential risks and ethical dilemmas. It is essential to use AI as a tool to support human educators rather than replace them entirely. The adoption of AI in education holds the promise of creating more inclusive and effective learning environments, catering to students of diverse backgrounds and abilities. As AI technology continues to advance, the education sector can anticipate even more innovative applications, further shaping the future of learning.This abstract provides an overview of the multifaceted landscape of AI in education, highlighting its potential benefits, associated challenges, and the importance of responsible integration.
Category: Artificial Intelligence
[2] viXra:2310.0015 [pdf] submitted on 2023-10-04 22:21:52
Authors: Stephane H. Maes
Comments: 5 Pages.
This short paper provides a short list of comments in answer to the request for public comments for the MPAI MMC (Multi-modal conversations) V2.Our concerns can be grouped in terms of questions on business value, on the architecture assumptions, the standardized artefacts, and the scope of the MMC use cases. Except for the latter, these comments can probably read, and apply to other drafts published by MPAI (MOVING PICTURE, AUDIO AND DATA CODING BY ARTIFICIAL INTELLIGENCE) and on-going activities.
Category: Artificial Intelligence
[1] viXra:2310.0006 [pdf] submitted on 2023-10-02 14:08:51
Authors: Satish Gajawada
Comments: 4 Pages.
Several Human-Inspired Metaheuristic Optimization Algorithms were proposed in literature. But the concept of Devotees-Inspired Metaheuristic Optimization Algorithms is not yet explored. In this article, Lord Rama Devotees Algorithm (LRDA) is proposed which is a new Devotees-Inspired Metaheuristic Optimization Algorithm.
Category: Artificial Intelligence
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