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Comparative analysis of deep learning and machine learning techniques for forecasting new malaria cases in Cameroon's Adamaoua region

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Intelligence-Based Medicine 2025年 11卷 100220-100220页

作者： Naroum, Esaie Maka, Ebenezer Maka Abboubakar, Hamadjam Dayang, Paul Bamana, Appolinaire Batoure Garga, Benjamin Daouda, Hassana Daouda Bakouri, Mohsen Khan, Ilyas University of Ngaoundéré Department of Mathematics and computer Science Faculty of Science P.O. Box 455 Ngaoundéré Cameroon University of Douala National Higher Polytechnic School Department of Computer Engineering and Telecommunications P.O. Box 2701 Douala Cameroon University of Ngaoundéré University Institute of Technology Department of Computer Engineering P.O. Box 455 Ngaoundéré Cameroon University of Ngaoundéré Laboratory of Analysis Simulation and Test (LASE) P.O. Box 455 Ngaoundéré Cameroon University of Ngaoundéré School of Geology and Mining Engineering Department Applied Mathematics and Computer Science P.O. Box 115 Meiganga Cameroon University of Ngaoundéré ENSAI Department of Electrical Electronic and Automatic Engineering P.O. Box 455 Ngaoundéré Cameroon Majmaah University College of Applied Medical Science Department of Medical Equipment Technology Al-Majmaah 11952 Saudi Arabia Department of Mathematics College of Science Al-Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia

The Plasmodium parasite, which causes malaria is transmitted by Anopheles mosquitoes, and remains a major development barrier in Africa. This is particularly true considering the conducive environment that promotes the spread of malaria. This study examines several machine learning approaches, such as long short term memory (LSTM), random forests (RF), support vector machines (SVM), and data regularization models including Ridge, Lasso, and ElasticNet, in order to forecast the occurrence of malaria in the Adamaoua region of Cameroon. The LSTM, a recurrent neural network variant, performed the best with 76% accuracy and a low error rate (RMSE = 0.08). Statistical evidence indicates that temperatures exceeding 34 degrees halt mosquito vector reproduction, thereby slowing the spread of malaria. However, humidity increases the morbidity of the condition. The survey also identified high-risk areas in Ngaoundéré Rural and Urban and Meiganga. Between 2018 and 2022, the Adamaoua region had 20.1%, 12.3%, and 10.0% of malaria cases, respectively, in these locations. According to the estimate, the number of malaria cases in the Adamaoua region will rise gradually between 2023 and 2026, peaking in 2029 before declining in 2031. © 2025 The Authors

关键词： Adamaoua-Cameroon Deep learning Machine learning Malaria Metric evaluation Time-series forecasting

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Machine Learning and IoT-Based Waste Management Model

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Computational Intelligence and Neuroscience 2021年 2021卷 5942574页

作者： Khan, Rijwan Kumar, Santosh Srivastava, Akhilesh Kumar Dhingra, Niharika Gupta, Mahima Bhati, Neha Kumari, Pallavi Department of Computer Science and Engineering Abes Institute of Technology U.P. Ghaziabad India Department of Mathematics College of Natural and Applied Sciences University of Dar Es Salaam Dar es Salaam Tanzania United Republic of Department of Computer Science and Engineering Abes Engineering College Ghaziabad India

A rapid rise in inhabitants across the globe has led to the inadmissible management of waste in various countries, giving rise to various health issues and environmental pollution. The waste-collecting trucks collect waste just once or twice in seven days. Due to improper waste collection practices, the waste in the dustbin is spread on the streets. Thus, to defeat this situation, an efficient solution for smart and effective waste management using machine learning (ML) and the Internet of Things (IoT) is proposed in this paper. In the proposed solution, the authors have used an Arduino UNO microcontroller, ultrasonic sensor, and moisture sensor. Using image processing, one can measure the waste index of a particular dumping ground. A hardware prototype is also developed for the proposed framework. Thus, the presented solution for the efficient management of waste accomplishes the aim of establishing clean and pollution-free cities. © 2021 Rijwan Khan et al.

关键词： Waste management

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Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

arXiv

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

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Business Process Automation using Robotic Process Automation (RPA) and AI Algorithm’s on Various Tasks

Business Process Automation using Robotic Process Automation...

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International Conference on Communication and Electronics Systems (ICCES)

作者： M. N. Dandale Mazharunnisa D. J. Joel Devadass Daniel R. Sathya Priya Md. Abul Ala Walid Thulasimani T Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India KL Business School Koneru Lakshmaiya Educational Foundation KL University Vijayawada Andhra Pradesh India Department of Electronics and Communication Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of CSE Knowledge Institute of Technology Salem Tamilnadu India Department of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Mathematics Bannari Amman Institute of Technology Erode Tamilnadu India

Robotic Process Automation (RPA) bots automate mundane, rules-based operations, allowing workers more time to focus on high-level, strategic projects. Meanwhile, AI be used to analyze data and spot trends, resulting in better choices and increased productivity for enterprises. Previous researches are less efficient, slowly working algorithms when classification is performed in a large set of databases. The existing methods could be doing better while comparing the error factors, and in the cross-verification process, they have made inappropriate results, leading to wrong classifications. Robotic Process Automation (RPA) bots automate mundane, rules-based operations, allowing workers more time to focus on high-level, strategic projects. Meanwhile, AI be used to analyze data and spot trends, resulting in better choices and increased productivity for enterprises. Previous research on email automation and invoice process automation have needed to improve classification model efficiency and they have less efficient, slowly working algorithms when doing classification in a large set of databases. In this work, the Random Forest algorithm is used for classification, and the Quest method is used to segment texts in emails and invoices, both of which can be automated more effectively. The results of existing categorization algorithms have been less than ideal, especially when used to huge datasets, and are often completely inaccurate. The suggested method outperforms previous ML/AI approaches because it produces highly accurate outcomes with little resource investment. There are a number of benefits to utilizing RPA with AI, such as cost reduction, increased output, and streamlined operations. The advantages of this automation, challenges that must be met, and potential answers to those questions are discussed in this study.

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Deep learning models for early and accurate diagnosis of ventilator-associated pneumonia in mechanically ventilated neonates

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computers in Biology and Medicine 2025年 189卷 109942-109942页

作者： Hsu, Jen-Fu Lin, Ying-Chih Lin, Chun-Yuan Chu, Shih-Ming Cheng, Hui-Jun Xu, Fan-Wei Huang, Hsuan-Rong Liao, Chen-Chu Fu, Rei-Huei Tsai, Ming-Horng Division of Neonatology and Pediatric Hematology/Oncology Department of Pediatrics Chang Gung Memorial Hospital Yunlin Taiwan Division of Pediatric Neonatology Department of Pediatrics Chang Gung Memorial Hospital Taoyuan Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of Applied Mathematics Feng Chia University Taichung Taiwan Department of Artificial Intelligence Application Minth University of Science and Technology HsinChu County Taiwan

Background: Early and accurate confirmation of critically ill neonates with a suspected diagnosis of ventilator-associated pneumonia (VAP) can optimize the therapeutic strategy and avoid unnecessary use of empirical antibiotics. We aimed to examine whether deep learning (DL) methods can assist the diagnosis of VAP of intubated neonates in the neonatal intensive care unit (NICU). Methods: A total of 670 neonates with mechanical ventilation were prospectively observed in a tertiary-level NICU in Taiwan between October 2017 and March 2022, during which image data were collected. All neonates with clinically suspected VAP were enrolled, and various DL methods were used to test the prediction ability of VAP diagnosis. The accuracy, precision, sensitivity, specificity, F1-score, and area under curves (AUCs) of several DL methods were compared. Results: A total of 900 chest X-ray images derived from 670 neonates with VAP and/or bronchopulmonary dysplasia (BPD) were enrolled, including 399 images from patients with definite diagnosis of VAP based on the strict criteria and 501 images from neonates without VAP. Compared with conventional DNN models such as ResNet, VGG, DenseNet, the RegNetX80 achieved the best specificity of 0.8378, which facilitates a low false positive rate. For accurate diagnosis of neonatal VAP, a combinatorial model of ResNet50 and RegNetX80, created through ensemble learning, further enhanced the AUC to 0.8023 for neonates with VAP on mechanical ventilation. In addition, the consistent XAI results in the left-lower region of chest X-ray image provided informative feedback and increased confidence to AI-assisted doctors. Conclusions: Deep learning methods are applicable with good predictive accuracy using chest X-ray images to help diagnosis of VAP in the NICU, which can help clinicians make decisions regarding the choices of empiric antibiotics for critically ill neonates. Future prospective trials are warranted to document its clinical usefulness and

关键词： Contrastive Learning

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Co-evolution of Viral Processes and Structural Stability in Signed Social Networks

arXiv

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arXiv 2021年

作者： Kalimzhanov, Temirlan Khamseh'i, Amir Haji Ali Dadlani, Aresh Kumar, Muthukrishnan Senthil Khonsari, Ahmad Nur-Sultan010000 Kazakhstan The School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran*** Iran The Department of Applied Mathematics and Computational Sciences PSG College of Technology Coimbatore641004 India

Prediction and control of spreading processes in social networks (SNs) are closely tied to the underlying connectivity patterns. Contrary to most existing efforts that exclusively focus on positive social user interactions, the impact of contagion processes on the temporal evolution of signed SNs (SSNs) with distinctive friendly (positive) and hostile (negative) relationships yet, remains largely unexplored. In this paper, we study the interplay between social link polarity and propagation of viral phenomena coupled with user alertness. In particular, we propose a novel energy model built on Heider's balance theory that relates the stochastic susceptible-alert-infected-susceptible epidemic dynamical model with the structural balance of SSNs to substantiate the trade-off between social tension and epidemic spread. Moreover, the role of hostile social links in the formation of disjoint friendly clusters of alerted and infected users is analyzed. Using three real-world SSN datasets, we further present a time-efficient algorithm to expedite the energy computation in our Monte-Carlo simulation method and show compelling insights on the effectiveness and rationality of user awareness and initial network settings in reaching structurally balanced local and global network energy states. © 2021, CC BY.

关键词： Monte Carlo methods

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Improved Attentive Pairwise Interaction (API-Net) for Fine-Grained Image Classification

Improved Attentive Pairwise Interaction (API-Net) for Fine-G...

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2021 Emerging Technology in Computing, Communication and Electronics, ETCCE 2021

作者： Yet, Ong Zu Rassem, Taha H. Rahman, Md Arafatur Rahman, M.M. Universiti Malaysia Pahang College of Computing and Applied Sciences Faculty of Computing Pekan Malaysia Bournemouth University Faculty of Science and Technology PooleBH12 5BB United Kingdom University of Wolverhampton School of Mathematics and Computer Science WolverhamptonWV1 1LY United Kingdom Universiti Malaysia Pahang College of Engineering Department of Mechanical Engineering Kuantan26300 Malaysia

ISBN: (纸本)9781665483643

Fine-grained classification is a challenging problem as one has to deal with a similar class of objects but with various types of variations. For more elaboration, they are almost similar and have subtle differences, and are confusing. In this study, aircraft will be the fine-grained object to be focused on. Aircraft which has almost similar shapes and patterns can be hardly recognized even for humans, especially those who haven not gone through any training. In recent years, a lot of proposed methods addressed to solve the difficulties in fine-grained problems by learning contrastive clues from an image. This study aims to increase the accuracy of the Attentive Pairwise Interaction Network (API-Net) by introducing data augmentation into the network structure. Some of the previous studies proved that data augmentation does help improve a network. So, this study is going to modify the API-Net with different data augmentation settings. In this study, various settings have been introduced to the API-Net. Several experiments had been done with a simple modification where a portion of the train dataset's images will randomly convert into greyscale images. These settings are, only brightness & contrast 0.2, only grayscale 0.3, only grayscale 0.5, brightness & contrast 0.2 with grayscale 0.3, and brightness & contrast 0.2 with grayscale 0.5. As a result, the proposed modification achieved with 92.74% with brightness & contrast 0.2, 92.80% on brightness & contrast 0.2 with grayscale 0.5, and 92.86% on brightness & contrast 0.2 with grayscale 0.3. While grayscale 0.3 alone achieve 93.25% and grayscale 0.5 alone achieve 93.46% compared with the original results which reached 92.77%. © 2021 IEEE.

关键词： Luminance

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Hybrid Time-Frequency Domain Analysis for Cardiovascular Disease Forecasting Over ECG Data 1st

Hybrid Time-Frequency Domain Analysis for Cardiovascular Dis...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Zaidi, Abdelhamid Byeon, Haewon Keshta, Ismail Soni, Mukesh Kumar, K. Keshav Garg, Ansh Department of Mathematics College of Science Qassim University P.O.Box 6644 Buraydah51452 Saudi Arabia Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil School of Science and Technology Tathawade Pune India College Telangana Hyderabad500 104 India Institute of Engineering and Technology Chitkara University Chitkara University Punjab India

ISBN: (纸本)9783031860683

Due to the few data samples available for computer-aided systems, arrhythmia, a common cardiovascular ailment, creates hurdles for accurate identification. This paper presents a unique methodology for feature extraction in arrhythmia classification that uses a hybrid time-frequency domain analysis within a Convolutional Neural Network (CNN) architecture. The suggested method combines temporal information from RR intervals, frequency-domain information from the Hilbert-Huang transformation, and combined time-frequency information from the continuous wavelet transformation. Following that, a CNN is trained with Focal Loss as the designated loss function. The approach has been carefully tested and verified on an arrhythmia database, proving its ability to classify four unique types of electrocardiogram (ECG) data. Empirical results show that the proposed hybrid time-frequency domain feature extraction method outperforms current classification methodologies in terms of accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Hilbert-Huang transform

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Ambiguous Fuzzy Averaging Operator Based Decision-Making Approach with Application to Classification of Renewable Energy Sources

Ambiguous Fuzzy Averaging Operator Based Decision-Making App...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Bhagawati Prasad Joshi Arvind Bhatt Mehul Manu Anuj Kumar Sangeeta Pant Pawan Joshi Department of Mathematics Graphic Era Hill University Bhimtal India Department of Mathematics Uttarakhand Open University Haldwani Uttrakhand India Department of Allied Sciences Graphic Era Hill University Bhimtal India School of Computer Science Engg. & Applications D. Y. Patil International University (DYPIU) Akrudi Pune Maharashtra Department of Applied Sciences Symbiosis International (Deemed University) (SIU) Lavale Pune Maharashtra Department of Applied Sciences Shivalik College of Engineering Dehradun India

A renewable-energy-source-selection (RES-selection) is a complex multi-criteria-decision-making (MCDM) challenge to select the optimal RES on the basis of some criteria. The complexity exists due to the relationship of miscellaneous contradictory standards, unknown and vague information. To deal with inherent uncertainties and vague information, the theory of Ambiguous intuitionistic fuzzy set (AIFS) has been introduced recently. This study is an effort to provide a RES-classification method under the AIFS environment. This can be achieved by proposing new aggregation operators, namely the ambiguous-intuitionistic weighted-averaging (AI- W A) and the ambiguous-intuitionistic ordered-weighted-averaging (AI-OW A), to fuse AIFNs input information. Based on the proposed the AI-WA and the AI-OW A operators, we develop an approach for RES- classification problem and applied it to classify four considered RESs.

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： A S Gowri P Jose Karthik S M S Berlin Shaheema K M Karuppasway R. Balamurugan Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department for Applied Research The Gandhigram Rural Institute (Deemed to be University) Gandhigram India Department of Artificial Intelligence and Data Science Stella Mary’s College of Engineering Azhikal India Department of Mathematics and Statistics Bharath Institute of Higher Education and Research Chennai India Department of Information Technology DMI College of Engineering Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%.

关键词： Support vector machines Accuracy Convolution Machine learning Feature extraction Data models Diabetes Data mining Convolutional neural networks Principal component analysis

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