Transactions on Machine Learning and Artificial Intelligence最新文献

: This article describes how the discovery of the universal mass-energy equivalence relation came about and tabulates the possible high, medium and low voltage Metal-Oxide-Semiconductor-Field-Effect-Transistors (MOSFETs) from different semiconductors which could be n-channel or p-channel devices. Some points are to be considered for the tabulated MOSFETs which are enlisted. The universal mass-energy equivalence relation is dE/E = dm/m, where E is the energy and m is the mass.

:这篇文章介绍了普遍质能方程关系的发现过程，并以表格形式列出了不同半导体的高、中、低压金属氧化物半导体场效应晶体管（MOSFET），这些器件可以是 n 沟道器件，也可以是 p 沟道器件。表中列出的 MOSFET 需要考虑一些要点。通用的质能等价关系为 dE/E = dm/m，其中 E 为能量，m 为质量。

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Multi-Layer Perceptron Algorithm, an Effective tool for the Prediction of the Judgments of the Supreme Court of Nigeria 多层感知器算法，尼日利亚最高法院判决预测的有效工具

Transactions on Machine Learning and Artificial Intelligence

Pub Date : 2023-11-13 DOI: 10.14738/tecs.116.15858

Effective dispensation of justice is sacrosanct to the sustenance of peace and stability of any nation. Justice delayed is often perceived as justice denied, and so it becomes important that the rule of law as it concerns effective and efficient justice delivery is sustained. In achieving this effectiveness, adherence to transparency and adequate knowledge of judicial proceedings and practices play a key part in achieving justice. This is however not the case with the Nigerian justice system, as court congestions and case delays have plagued the Nigerian Supreme Court for decades, breeding distraught and lack of confidence in the institution and its process. This study attempted to quicken the pace of justice delivery by developing a predictive model for the classification of Supreme Court judgments in Nigeria, and improved the performance of the computational process that is required for the identification of the pattern between feature and judgment using the Pearson Correlation Coefficient to select relevant features. The study which was aimed at developing a predictive model for the classification of Supreme Court judgments in Nigeria using Multilayer Perceptron (MLP) algorithm was carried out using 5585 records of precedent judgments delivered at the SCN between 1962- July 2022. Data was collected from an independently owned data repository (Primsol Law Pavilion). Data annotation and feature extraction were carried out and variables that have strong impact on judgments were identified both from literature and from domain experts. Pearson Correlation feature selection method was used to select the most relevant features from the initially identified features, after which multi-layer perceptron with ADAM optimization function was used to develop the classification model. The result of the feature selection algorithms revealed that the Pearson Correlation-based methods proved to be effective in the identification of the most relevant features. The MLP-ADAM model for predicting the outcome of Supreme Court judgment was evaluated and benchmarked with a related study carried out to predict judicial decisions of criminal cases from Thai Supreme Court, using both conventional and modified models. The result of MLP-ADAM showed a better performance of predicting judicial decision, showing 100% precision, 99% recall and 100% F1 Score for the as against 69.59% precision,79.87% recall and 74.38% F1score obtained by the Bi-GRU + attention model of the existing study. The study showed that feature selection using the Pearson correlation based approach provides a better performance. The study also revealed that the ADAM optimization function was significant in achieving good accuracy and generalization ability of the model. The use of structured and well organized dataset enabled the model to train effectively. The study also demonstrated that a higher proportion of the dataset is important in the training phase.

有效的司法公正对任何国家的和平与稳定都是不可侵犯的。拖延的司法往往被视为剥夺了司法，因此，重要的是要维持涉及有效和高效司法的法治。为了实现这种有效性，坚持透明度和充分了解司法程序和惯例在实现正义方面发挥了关键作用。然而，尼日利亚司法系统的情况并非如此，因为法院拥挤和案件延误几十年来一直困扰着尼日利亚最高法院，导致人们对该机构及其程序感到不安和缺乏信心。本研究试图通过开发尼日利亚最高法院判决分类的预测模型来加快司法交付的速度，并改进了使用Pearson相关系数选择相关特征来识别特征与判决之间模式所需的计算过程的性能。该研究旨在使用多层感知器(MLP)算法开发尼日利亚最高法院判决分类的预测模型，使用1962年至2022年7月期间在SCN交付的5585份先例判决记录进行。数据收集自一个独立拥有的数据存储库(Primsol Law Pavilion)。进行了数据标注和特征提取，并从文献和领域专家中识别出对判断有强烈影响的变量。采用Pearson相关性特征选择方法从初始识别的特征中选择相关度最高的特征，然后使用带有ADAM优化函数的多层感知器建立分类模型。特征选择算法的结果表明，基于Pearson相关性的方法在识别最相关的特征方面是有效的。预测最高法院判决结果的MLP-ADAM模型与一项预测泰国最高法院刑事案件司法判决的相关研究进行了评估和基准测试，使用传统模型和改进模型。结果表明，MLP-ADAM在预测司法判决方面表现出了较好的效果，对司法判决的预测准确率为100%，召回率为99%，F1分数为100%。现有研究中Bi-GRU +注意力模型的准确率为69.59%，召回率为79.87%，F1score为74.38%。研究表明，使用基于Pearson相关性的方法进行特征选择可以提供更好的性能。研究还表明，ADAM优化函数在获得良好的模型精度和泛化能力方面具有重要意义。使用结构化和组织良好的数据集使模型能够有效地训练。研究还表明，在训练阶段，更高比例的数据集是重要的。

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引用次数: 0

On the Density of Primes of the form X^2+c 关于 X^2+c 形式的素数密度

Transactions on Machine Learning and Artificial Intelligence

Pub Date : 2023-11-07 DOI: 10.14738/tecs.116.15890

Marc Wolf, Franccois Wolf

We present a method for finding large fixed-size primes of the form $X^2+c$. We study the density of primes on the sets $E_c = {N(X,c)=X^2+c, X in (2mathbb{Z}+(c-1))}$, $c in mathbb{N}^*$. We describe an algorithm for generating values of $c$ such that a given prime $p$ is the minimum of the union of prime divisors of all elements in $E_c$. We also present quadratic forms generating divisors of Ec and study the prime divisors of its terms. This paper uses the results of Dirichlet's arithmetic progression theorem [1] and the article [6] to rewrite a conjecture of Shanks [2] on the density of primes in $E_c$. Finally, based on these results, we discuss the heuristics of large primes occurrences in the research set of our algorithm.

我们提出了一种寻找 $X^2+c$ 形式的固定大小大素数的方法。我们研究了 $E_c = {N(X,c)=X^2+c,X in (2mathbb{Z}+(c-1))}$, $c in mathbb{N}^*$集合上的素数密度。我们描述了一种生成 $c$ 值的算法，这样一个给定的素数 $p$ 就是 $E_c$ 中所有元素的素除之和的最小值。我们还提出了生成 Ec 除数的二次型，并研究了其项的素除数。本文利用狄利克特算术级数定理 [1] 和文章 [6] 的结果，重写了香克斯 [2] 关于 $E_c$ 中素数密度的猜想。最后，基于这些结果，我们讨论了我们算法研究集中大素数出现的启发式方法。

引用次数: 0

Convolutional Neural Networks Model for Medical Radiographic Image Recognition COVID-19 Cases of Madagascar 卷积神经网络模型在医学放射图像识别中的应用

Transactions on Machine Learning and Artificial Intelligence

Pub Date : 2023-11-05 DOI: 10.14738/tecs.115.15678

The symptoms related to COVID-19 are diverse depending on the severity of the disease. COVID-19 is responsible for a clinical picture called the coronavirus, named SARS-CoV-2 by the who, which involves multiple organ systems, including the lungs. To determine if the lungs are affected, the doctor relies on radiographic images and its interpretation requires a specialist physician. Our research work proposes an artificial intelligence-based system to replace the specialist doctor in order to provide an interpretation of the obtained image and address the problems of a shortage of qualified doctors (radiologists). Indeed, a convolutional neural network has been proposed to train data from real images for cases of patients diagnosed with COVID or not, based on real data COVID-19 in Madagascar. Various parameters of the network were adjusted to obtain an efficient neural network model. Due to a shortage of image data and the limited computing resources (CPU and memory) of our machine, and in order to achieve sufficient performance, we used the transfer learning technic, which involves reusing a pretrained model capable to classify and adapte images to our own model. Our validation shows that the obtained model provides better classification.

根据疾病的严重程度，与COVID-19相关的症状多种多样。COVID-19导致了一种名为冠状病毒的临床症状，世界卫生组织将其命名为SARS-CoV-2，它涉及包括肺在内的多个器官系统。为了确定肺部是否受到影响，医生依靠放射图像，其解释需要专科医生。我们的研究工作提出了一种基于人工智能的系统来取代专科医生，以提供对获得的图像的解释，并解决合格医生(放射科医生)短缺的问题。事实上，已经提出了一种卷积神经网络，以马达加斯加的真实COVID-19数据为基础，从真实图像中训练数据，以确定是否诊断为COVID-19的患者。通过对网络各参数的调整，得到一个高效的神经网络模型。由于图像数据的短缺和机器有限的计算资源(CPU和内存)，为了达到足够的性能，我们使用了迁移学习技术，这涉及到重用一个预训练的模型，该模型能够对图像进行分类并适应我们自己的模型。我们的验证表明，得到的模型提供了更好的分类。

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