Transactions on Machine Learning and Artificial Intelligence最新文献

{"title":"Singing Voice Melody Detection","authors":"","doi":"10.14738/tecs.123.16927","DOIUrl":"https://doi.org/10.14738/tecs.123.16927","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141125750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing Challenges Encountered by English Language Teachers in Imparting Communication Skills among Higher Secondary Students: A Critical Overview","authors":"","doi":"10.14738/tecs.123.16915","DOIUrl":"https://doi.org/10.14738/tecs.123.16915","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141125321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inquiring About The Memetic Relationships People Have with Societal Collapse","authors":"","doi":"10.14738/tecs.121.16355","DOIUrl":"https://doi.org/10.14738/tecs.121.16355","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140265728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural Ventilation in a Semi-Confined Enclosure Heated by a Linear Heat Source","authors":"","doi":"10.14738/tecs.121.16390","DOIUrl":"https://doi.org/10.14738/tecs.121.16390","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NMC: A Fast and Secure ARX Cipher","authors":"","doi":"10.14738/tecs.116.15857","DOIUrl":"https://doi.org/10.14738/tecs.116.15857","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139225716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowerment Faculty and Institutional Leaders with Autonomy and Accountability, and Enhance their Professional Development and Career Progression as per NEP-2020 - Higher Education Transformations in India","authors":"","doi":"10.14738/tecs.116.15866","DOIUrl":"https://doi.org/10.14738/tecs.116.15866","url":null,"abstract":"","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139259772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Discovery of the Universal Mass-Energy Equivalence Relation in Materials Having a Bandgap","authors":"D. R. K. Chanana","doi":"10.14738/tecs.116.15906","DOIUrl":"https://doi.org/10.14738/tecs.116.15906","url":null,"abstract":": 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.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139260166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Layer Perceptron Algorithm, an Effective tool for the Prediction of the Judgments of the Supreme Court of Nigeria","authors":"","doi":"10.14738/tecs.116.15858","DOIUrl":"https://doi.org/10.14738/tecs.116.15858","url":null,"abstract":"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.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Density of Primes of the form X^2+c","authors":"Marc Wolf, Franccois Wolf","doi":"10.14738/tecs.116.15890","DOIUrl":"https://doi.org/10.14738/tecs.116.15890","url":null,"abstract":"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.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139286559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convolutional Neural Networks Model for Medical Radiographic Image Recognition COVID-19 Cases of Madagascar","authors":"","doi":"10.14738/tecs.115.15678","DOIUrl":"https://doi.org/10.14738/tecs.115.15678","url":null,"abstract":"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.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135724993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}