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Enhancing Spam Email Classification using Multilayer Perceptron: Performance Analysis and Comparative Evaluation

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Enhancing Spam Email Classification using Multilayer Percept...

International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Ghizlane Khababa Nor Hazlyna Harun Sadik Bessou Chu Sin Ning Ooi Poa Ning Department of Computer Sciences Faculty of Sciences Sétif 1 University Sétif Algeria Data Science Research Lab (DSRL) School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia School of Computing Universiti Utara Malaysia Sintok Kedah Malaysia

With the rapid increase in spam email volume and the associated productivity and security risks, effective spam email classification mechanisms are crucial in ensuring smooth communication systems. After several studies on spam email classification, the outcome demonstrated that Multilayer Perceptron (MLP) outperforms SVM and Decision Trees, making it the chosen model for further research in this paper, as it presented a study on spam email classification using a multilayer perceptron (MLP) model. The proposed MLP-based approach demonstrates strong performance in distinguishing between legitimate and spam emails. A comprehensive evaluation is conducted using a dataset of 5,172 emails, resulting in an accuracy rate of 94.40% on the testing set. The analysis of the results reveals interesting patterns and trends in the data, providing valuable insights into the effectiveness of the MLP model. Furthermore, a comparison with some state-of-the-art approaches showcases the superiority of the MLP model in terms of accuracy, F1 score, precision, recall, and MSE. The results of this study aid in the creation of reliable and precise spam email classification systems, increasing productivity and defending communication channels against spam threats.

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Large Language Models for Mathematicians

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arXiv 2023年

作者： Frieder, Simon Berner, Julius Petersen, Philipp Lukasiewicz, Thomas Department of Computer Science University of Oxford Oxford United Kingdom Department of Computing and Mathematical Sciences Caltech Pasadena United States Faculty of Mathematics and Research Network Data Science University of Vienna Vienna Austria Institute of Logic and Computation Vienna University of Technology Vienna Austria

Large language models (LLMs) such as ChatGPT have received immense interest for their general-purpose language understanding and, in particular, their ability to generate high-quality text or computer code. For many professions, LLMs represent an invaluable tool that can speed up and improve the quality of work. In this note, we discuss to what extent they can aid professional mathematicians. We first provide a mathematical description of the transformer model used in all modern language models. Based on recent studies, we then outline best practices and potential issues and report on the mathematical abilities of language models. Finally, we shed light on the potential of LLMs to change how mathematicians work. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

CONSTRAINED CONSENSUS-BASED OPTIMIZATION AND NUMERICAL HEURISTICS FOR THE FEW PARTICLE REGIME

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arXiv 2024年

作者： Beddrich, Jonas Chenchene, Enis Fornasier, Massimo Huang, Hui Wohlmuth, Barbara Faculty of Mathematics University of Vienna Austria Department of Mathematics Munich Data Science Institute Technical University of Munich Garching by Munich & Munich Center for Machine Learning Munich Germany Department of Mathematics and Scientific Computing University of Graz Austria Department of Mathematics Technical University of Munich Garching by Munich Germany

Consensus-based optimization (CBO) is a versatile multi-particle optimization method for performing nonconvex and nonsmooth global optimizations in high dimensions. Proofs of global convergence in probability have been achieved for a broad class of objective functions in unconstrained optimizations. In this work we adapt the algorithm for solving constrained optimizations on compact and unbounded domains with boundary by leveraging emerging reflective boundary conditions. In particular, we close a relevant gap in the literature by providing a global convergence proof for the many-particle regime comprehensive of convergence rates. On the one hand, for the sake of minimizing running cost, it is desirable to keep the number of particles small. On the other hand, reducing the number of particles implies a diminished capability of exploration of the algorithm. Hence numerical heuristics are needed to ensure convergence of CBO in the few-particle regime. In this work, we also significantly improve the convergence and complexity of CBO by utilizing an adaptive region control mechanism and by choosing geometry-specific random noise. In particular, by combining a hierarchical noise structure with a multigrid finite element method, we are able to compute global minimizers for a constrained p-Allen–Cahn problem with obstacles, a very challenging variational *** Codes 90C26, 90C56, 90C59, 65K10, 35Q90, 60H10 © 2024, CC BY.

关键词： Constrained optimization

TransformerFusionNet: A Real-Time Multimodal Framework for ICU Heart Failure Mortality Prediction Using Big data Streaming

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TransformerFusionNet: A Real-Time Multimodal Framework for I...

International Conference on Computer and Applications (ICCA)

作者： Hager Saleh Michael McCann Shaker El-Sappagh John G. Breslin Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt Insight Research Ireland Centre for Data Analytics University of Galway Galway Ireland Atlantic Technological University Letterkenny Ireland Department of Computing Atlantic Technological University Letterkenny Ireland Faculty of Computer Science and Engineering Galala University Suez Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt School of Engineering University of Galway Galway Ireland

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This paper presents a real-time multimodal frame-work to enhance ICU mortality prediction for heart disease patients by integrating structured data, clinical notes, and big data streaming platforms. The proposed framework comprises two components: an offline multimodal model and a real-time pipeline. In the offline component, we proposed the Transformer-FusionNet model leverages BioBERT-Transformer for clinical notes and a Recurrent Neural Network (RNN) for structured data. The model integrates outputs through a concatenation layer to make predictions and is rigorously evaluated using the MIMIC-III dataset. It demonstrates superior performance across evaluation metrics and achieves an accuracy of 91.720%, precision of 91.85%, recall of 91.720%, and F1-score of 91.693%, outperforming other single and multimodal models. The real-time pipeline component incorporates Apache Spark and Apache Kafka for ingesting, preprocessing, and streaming structured data and clinical notes. This integration enables real-time mor-tality prediction with TransformerFusionNet, addressing critical gaps in the utilization of clinical notes in healthcare analytics. The framework significantly advances multimodal deep learning applications in healthcare by combining state-of-the-art models with scalable streaming technologies, offering a practical solution for improving clinical decision support systems and patient outcomes in real-time, especially in intensive care unit settings.

关键词： Heart Accuracy Recurrent neural networks Biological system modeling Pipelines Mortality Medical services Big data Transformers Real-time systems

Determining environmental indicators related to the propagation of contagious diseases and health issues: A Systematic Literature Review

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Determining environmental indicators related to the propagat...

Iberian Conference on Information Systems and Technologies (CISTI)

作者： Janeth Domínguez Portillo Laura M. Rodríguez Peralta Paulo N. M. Sampaio Eldman de Oliveira Nunes Faculty of Information Technologies and Data Science Universidad Popular Autónoma del Estado de Puebla (UPAEP) México Graduate Program in Computing and Systems University of Salvador (UNIFACS) Salvador Bahia Brazil

The environmental indicators provide significant information in order to describe the state of a phenomenon, environment or area [1]. This paper addresses the indicators present in the air and their relationship in the accommodation and transmission of viruses, as well as how these environmental indicators can lead to health problems. Therefore, the main goal of this work is to review the scientific contributions in the literature in order to determine and select the potential environmental indicators present in indoor environments which have an impact on the health of people who work and live within these spaces. For this purpose, we carried out a Systematic Literature Review (SLR) related to environmental monitoring, COVID-19, Internet of things, air pollution and environmental indicators within indoor spaces. This SLR allowed us to identify indicators that according to their level of presence can represent a high risk for people’s health. Therefore, the process of planning, conducting and reporting the Systematic Literature Review enabled us not only to identify some environmental indicators, but also their corresponding monitoring advantages and disadvantages, and multiple perspectives and approaches of each contribution in the literature. Further on, this study will be the basis for the proposal of an automatic and intelligent platform based on Internet of Things for monitoring indoor environments in order to detect high risk levels for the contamination of contagious diseases.

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When Artificial Intelligence meets Navarasas - Facial Expression Classification in Kathakali Using DSFD and CNN

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When Artificial Intelligence meets Navarasas - Facial Expres...

IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Ganesh Neelakanta Iyer Nattapong Saengarunvong Minh Hien Le Department of Computer Science School of Computing National University of Singapore Singapore Department of Computer Engineering Faculty of Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand Department of Computer and Data Sciences Case School of Engineering Case Western Reserve University Cleveland Ohio USA

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

Kathakali, an ancient art form that dates back to the 17th century, is renowned for its intricate hand gestures, dance movements, musical accompaniment, elaborate costumes, and makeup. Mastering such an art form is a challenge even for the most dedicated enthusiasts, given its unconventional methods of expression. However, with current advanced technology, we can use this to aid the learning of unfamiliar subjects by classifying factors to help the new learners. In this paper, we focus on classifying one aspect of the play, which is the expressions on the actor’s face given a picture using computer vision methods. To achieve the final model, we use a combination of Dual Shot Face Detector (DSFD) and Convolution Neural Network (CNN) and finally tuning up the epochs of the training process. With our dataset, the proposed model reached an accuracy of 93% among nine classes. This project hopes to introduce Kathakali to a wider audience and facilitate the learning process of this art form.

关键词： Training Accuracy Art Computational modeling Signal processing algorithms Detectors data models Faces Tuning Testing

Competitive Facility Location under Cross-Nested Logit Customer Choice Model: Hardness and Exact Approaches

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arXiv 2024年

作者： Le, Ba Luat Mai, Tien Ta, Thuy Anh Ha, Minh Hoang Vu, Duc Minh ORLab Faculty of Computer Science Phenikaa University Hanoi Ha Dong Viet Nam School of Computing and Information Systems Singapore Management University 80 Stamford Rd Singapore178902 Singapore Faculty of Data Science and Artificial Intelligence NEU College of Technology Hai Ba Trung Hanoi Viet Nam ORLab-SLSCM National Economics University Hai Ba Trung Hanoi Viet Nam

We study the competitive facility location problem, where a firm aims to establish opening facilities in a market already occupied by competitors. In this problem, customer behavior is a crucial factor in making optimal location decisions. We explore a general class of customer choice models, known as the cross-nested logit model, which is recognized for its flexibility and generality in predicting people’s choice behavior. To explore the problem, we first demonstrate that it is NP-Hard, even when there is only one customer class. We further show that this hardness result is tight, as the facility location problem under any simpler choice models (such as the logit or nested logit) is polynomial-time solvable when there is one customer class. To tackle the resulting facility location problem, we demonstrate that the objective function under a general cross-nested structure is not concave. Interestingly, we show that by a change of variables, the objective function can be converted to a convex program (i.e., a maximization problem with a concave objective and convex constraints), enabling it to be solved to optimality via an outer-approximation algorithm. Extensive experiments show the efficiency of our approach and provide analyses on the benefits of using the cross-nested model in the facility location context. Copyright © 2024, The Authors. All rights reserved.

关键词： Sales

A new computational model for quantifying blood flow dynamics across myogenically-active cerebral arterial networks

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arXiv 2024年

作者： Coccarelli, Alberto Polydoros, Ioannis Drysdale, Alex Harraz, Osama F. Kadapa, Chennakesava Zienkiewicz Institute for Modelling Data and AI Faculty of Science and Engineering Swansea University Swansea United Kingdom Department of Mechanical Engineering Faculty of Science and Engineering Swansea University Swansea United Kingdom Department of Pharmacology Larner College of Medicine Vermont Center for Cardiovascular and Brain Health University of Vermont Burlington United States School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh United Kingdom

Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the involved cellular processes are mechanically driven, the quantification of haemodynamic forces in in-vivo settings remains extremely difficult and uncertain. In this work, we propose a novel computational framework for evaluating the blood flow dynamics across networks of myogenically active cerebral arteries, which can modulate their muscular tone to stabilize flow (and perfusion pressure) as well as to limit vascular intramural stress. The introduced framework is built on contractile (myogenically active) vascular wall mechanics and blood flow dynamics models, which can be numerically coupled in either a weak or strong way. We investigate the time dependency of the vascular wall response to pressure changes at both single vessel and network levels. The robustness of the model was assessed by considering different types of inlet signals and numerical settings in an idealized vascular network formed by a middle cerebral artery and its three generations. For the vessel size and boundary conditions considered, weak coupling ensured accurate results with a lower computational cost. To complete the analysis, we evaluated the effect of an upstream pressure surge on the haemodynamics of the vascular network. This provided a clear quantitative picture of how pressure and flow are redistributed across each vessel generation upon inlet pressure changes. This work paves the way for future combined experimental-computational studies aiming to decipher cerebral autoregulation. © 2024, CC BY.

关键词： Hemodynamics

Development of Advanced Noise Filtering Techniques for Medical Image Enhancement

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Development of Advanced Noise Filtering Techniques for Medic...

Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Sumit Kushwaha K. Amuthachenthiru Geetha. K Jonnadula Narasimharao Dileep Kumar M Sai Sudha Gadde Department of Computer Applications University Institute of Computing Chandigarh University Punjab India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Faculty of Management Sciences Hensard University Bayelsa Nigeria Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Telangana India

Medical imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), X-rays, and ultrasound, are extensively employed in the healthcare industry for diagnostic purposes. Noise, on the other hand, can disturb these approaches and lead to inaccurate diagnosis. Due to this problem, the significance of noise removal technologies has grown in the field of medical imaging, specifically for the examination and understanding of medical images and anatomical structures. To tackle these challenges, a variety of denoising techniques have been developed, such as the Weiner filter (WF), Gaussian filter, and median filter. This work employs a Deep Learning (DL) method known as Convolutional Autoencoder (CAE) to remove noise from medical images. We gather chest X-ray images for analysis and focus on common noise types that affect medical images, such as Poisson and Gaussian noise. We evaluate the effectiveness of the suggested denoising method CAE by comparing it to the commonly employed WF methodology. The Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are employed to assess and compare the performance of the WF and CAE in reducing Poisson and Gaussian noise. The study's findings indicate that the suggested denoising method based on CAE shows promising results in terms of improving quality.

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