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Machine Learning-based USD/PKR Exchange Rate Forecasting Using Sentiment Analysis of Twitter Data

computers, Materials & Continua 2021年第6期67卷 3451-3461页

作者： Samreen Naeem Wali Khan Mashwani Aqib Ali M.Irfan Uddin Marwan Mahmoud Farrukh Jamal Christophe Chesneau Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Faculty of Applied Studies King Abdulaziz UniversityJeddah21577Saudi Arabia Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Mathematics Universitéde CaenLMNOCampus IIScience 3Caen14032France

This study proposes an approach based on machine learning to forecast currency exchange rates by applying sentiment analysis to messages on Twitter(called tweets).A dataset of the exchange rates between the United States Dollar(USD)and the Pakistani Rupee(PKR)was formed by collecting information from a forex website as well as a collection of tweets from the business community in Pakistan containing finance-related *** dataset was collected in raw form,and was subjected to natural language processing by way of data *** variable labeling was then applied to the standardized dataset,where the response variables were divided into two classes:“1”indicated an increase in the exchange rate and“−1”indicated a decrease in *** better represent the dataset,we used linear discriminant analysis and principal component analysis to visualize the data in three-dimensional vector *** that were obtained using a sampling approach were then used for data *** machine learning classifiers—the simple logistic classifier,the random forest,bagging,naïve Bayes,and the support vector machine—were applied to the optimized *** results show that the simple logistic classifier yielded the highest accuracy of 82.14%for the USD and the PKR exchange rates forecasting.

关键词： Machine learning exchange rate sentiment analysis linear discriminant analysis principal component analysis simple logistic

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Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19 Monitoring System

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computers, Materials & Continua 2023年第2期74卷 2889-2903页

作者： Mahmoud Ragab Mohammed W.Al-Rabia Sami Saeed Binyamin Ahmed A.Aldarmahi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia Centre for Artificial Intelligence in Precision Medicines King Abdulaziz UniversityJeddah 21589Saudi Arabia Mathematics Department Faculty of ScienceAl-Azhar UniversityNaser City 11884CairoEgypt Department of Medical Microbiology and Parasitology Faculty of MedicineKing Abdulaziz UniversityJeddah 21589Saudi Arabia Health Promotion Center King Abdulaziz UniversityJeddah 21589Saudi Arabia Computer and Information Technology Department The Applied CollegeKing Abdulaziz UniversityJeddah 21589Saudi Arabia Basic Science Department College of Science and Health ProfessionsKing Saud Bin Abdulaziz University for Health SciencesJeddah 21423Saudi Arabia King Abdullah International Medical Research Center Ministry of National Guard-Health AffairsJeddah 21423Saudi Arabia

With the increasing and rapid growth rate of COVID-19 cases,the healthcare scheme of several developed countries have reached the point of *** important and critical steps in fighting against COVID-19 is powerful screening of diseased patients,in such a way that positive patient can be treated and isolated.A chest radiology image-based diagnosis scheme might have several benefits over traditional *** accomplishment of artificial intelligence(AI)based techniques in automated diagnoses in the healthcare sector and rapid increase in COVID-19 cases have demanded the requirement of AI based automated diagnosis and recognition *** study develops an Intelligent Firefly Algorithm Deep Transfer Learning Based COVID-19Monitoring System(IFFA-DTLMS).The proposed IFFADTLMSmodelmajorly aims at identifying and categorizing the occurrence of COVID19 on chest *** attain this,the presented IFFA-DTLMS model primarily applies densely connected networks(DenseNet121)model to generate a collection of feature *** addition,the firefly algorithm(FFA)is applied for the hyper parameter optimization of DenseNet121 ***,autoencoder-long short term memory(AE-LSTM)model is exploited for the classification and identification of *** ensuring the enhanced performance of the IFFA-DTLMS model,a wide-ranging experiments were performed and the results are reviewed under distinctive *** experimental value reports the betterment of IFFA-DTLMS model over recent approaches.

关键词： COVID-19 artificial intelligence intelligent systems deep learning decision making

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Segmentation and Classification of Stomach Abnormalities Using Deep Learning

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computers, Materials & Continua 2021年第10期69卷 607-625页

作者： Javeria Naz Muhammad Attique Khan Majed Alhaisoni Oh-Young Song Usman Tariq Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistanl 2College of Computer Science and EngineeringUniversity of Ha’ilHa’ilSaudi Arabial 3Department of SoftwareSejong UniversitySeoulKoreaGwangjin-guKoreal 4College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabial 5Faculty of Applied Computing and TechnologyNoroff University CollegeKristiansandNorway

An automated system is proposed for the detection and classification of GI *** proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep *** first bleeding region is segmented using a hybrid *** threshold is applied to each channel extracted from the original RGB ***,all channels are merged through mutual information and pixel-based *** a result,the image is *** and deep learning features are extracted in the proposed classification *** transfer learning(TL)approach is used for the extraction of deep *** Local Binary Pattern(LBP)method is used for texture ***,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture *** selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning *** experimental process is evaluated on the basis of two datasets:Private and *** accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data *** can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.

关键词： Gastrointestinal tract contrast stretching segmentation deep learning features selection

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Private Data Manipulation in Sponsored Search Auctions

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CAAI Artificial Intelligence Research 2023年第1期2卷 114-122页

作者： Xiaotie Deng Tao Lin Tao Xiao Center on Frontiers of Computing Studies Department of Computer SciencePeking UniversityBeijing 100871China School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Huawei TCS Lab Shanghai 201206China

The repeated nature of sponsored search auctions allows the seller to implement Myerson’s auction to maximize revenue using past *** since these data are provided by strategic buyers in the auctions,they can be manipulated,which may hurt the seller’s *** model this problem as a Private Data Manipulation(PDM)game:the seller first announces an auction(such as Myerson’s)whose allocation and payment rules depend on the value distributions of buyers;the buyers then submit fake value distributions to the seller to implement the *** seller’s expected revenue and the buyers’expected utilities depend on the auction rule and the game played among the buyers in their choices of the submitted *** the PDM game,we show that Myerson’s auction is equivalent to the generalized first-price auction,and under further assumptions equivalent to the Vickrey-Clarke-Groves(VCG)auction and the generalized second-price *** results partially explain why Myerson’s auction is not as popular as the generalized second-price auction in the practice of sponsored search auctions,and provide new perspectives into data-driven decision making in mechanism design.

关键词： Internet economics sponsored search auction Myerson’s auction generalized first-price auction data-driven decision making

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Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases 16th

Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate a...

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16th International Conference on Brain Informatics, BI 2023

作者： Shaffi, Noushath Vimbi, Viswan Mahmud, Mufti Subramanian, Karthikeyan Hajamohideen, Faizal College of Computing and Information Sciences University of Technology and Applied Sciences-Sohar OM 311 Sohar Oman Department of Computer Science Nottingham Trent University NottinghamNG11 8NS United Kingdom CIRC and MTIF Nottingham Trent University NottinghamNG11 8NS United Kingdom

ISBN: (纸本)9783031430749

Deep Learning (DL) techniques have shown promise in the early detection of neurodegenerative diseases due to their ability to analyze large amounts of medical data accurately. However, their reliance on massive training data may not be ideal in the healthcare industry. Therefore, this paper proposes a simple yet effective machine learning (ML) based hybrid ensemble of KNN and SVM for the early detection of Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). The proposed method is hybrid in the sense that it combines the strengths of both non-parametric and parametric approaches, resulting in a more robust and accurate classification performance. The method is tested on two popular AD databases, ADNI and OASIS, and the NTUA PD dataset. The hybrid ensemble method achieves higher accuracy and specificity for AD and PD detection, which is on par with popular DL algorithms. The source code for this work can be accessed at https://***/snoushath/***. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Optimizer: A Study 3

Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Opti...

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3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Sankaran, D. Abed Mohammed, Mazin Chander, Shrinithi Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Skin is a major sensory organs and the irregularity in the skin causes various issues. Skin cancer is one major medical emergency, and appropriate detection and treatment are essential. Clinical level skin cancer assessment is normally performed using the Dermoscopy Images (DI). This research aims to develop and implement the VGG-UNet scheme to extract and assess the abnormal section in DI with improved accuracy. This plan includes;(i) Image collection and resizing, (ii) Segmentation of skin lesion in DI, (iii) Comparing the segmented section with the Ground-Truth (GT), and (iv) Performance evaluation and validation. The merit of the proposed technique is confirmed by computing the necessary performance metrics. In this work, the performance of the proposed technique is verified with different optimizers (Adam/SGD) and the average pooling. The experimental work is executed on the ISIC2016 challenge dataset, and the merit of this scheme is verified with Jaccard, Dice, and Accuracy. © 2022 IEEE.

关键词： Diseases

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A Platform for Automated Solar Data Analysis Using Machine Learning

A Platform for Automated Solar Data Analysis Using Machine L...

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International Conference on Social Networks Analysis, Management and Security (SNAMS)

作者： Ola Younis Yahya Tashtoush Mohammad Alomari Omar Darwish Department of Computer Science University of Liverpool Liverpool UK Computer Science Department Jordan University of Science and Technology Jordan University of Liverpool UK UK Applied Science Research Center Applied Science Private University Jordan Information Security and Applied Computing Eastern Michigan University Ypsilanti USA

This paper presents a computer platform for the automated analysis of associations among different solar events and activities. This computer tool enables the advanced learning by implementing many associations' algorithms to analyze years of solar catalogues data and to study the associations among solar flares, eruptive filaments per prominences and Coronal Mass Ejections (CMEs). The aim is to combine all solar data catalogues in one dynamic space weather database that can be easily used in the analysis of solar activities and features. The computer tool identifies patterns of associations and provides numerical representations that can be used as inputs to the machine learning algorithms to provide computerized learning rules that can be developed in the future within the context of a real-time prediction system.

关键词： Machine learning algorithms Databases Social networking (online) Heuristic algorithms Machine learning Software Real-time systems

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Cyberbullying Detection Using Deep Learning: A Comparative Study

Cyberbullying Detection Using Deep Learning: A Comparative S...

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Cyber Resilience (ICCR), International Conference on

作者： Mouhammd Alkasassbeh Ammar Almomani Amjad Aldweesh Ahmad Al-Qerem Mohammad Alauthman Khalid Nahar Beenu Mago Computer Science Department Princess Sumaya University for Technology Amman Jordan School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Faculty of I.T Yarmouk University Irbid Jordan School of Computing Skyline University College University City of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Cyberbullying has become a serious issue with the rise in social media usage, especially among adolescents. Victims of cyberbullying often suffer from psychological distress, anxiety, depression and even suicidal tendencies. With the rapid growth of user-generated content online, manual detection of cyberbullying is extremely difficult. Hence, automated cyber-bullying detection systems are needed. In recent years, deep learning models have emerged as a promising technique for cyberbullying detection. This paper provides a comprehensive review of the use of deep learning for cyberbullying detection. The key contributions are: (1) a taxonomy of cyberbullying detection techniques, (2) a review of datasets used for model training and evaluation, (3) a comparative analysis of various deep learning models including CNN, RNN, LSTM, GRU and Transformer- based models, (4) a discussion of key challenges and limitations, and (5) suggestions for future research directions. The paper provides a critical perspective on model performance across multiple datasets. Key findings indicate RNNs and specifically LSTM and GRU models demonstrate state-of-the-art performance for sequential cyberbullying detection. The study concludes that deep ensemble models combining LSTMs and pretrained contextual embeddings show significant promise to advance the field.

关键词： Deep learning Training Analytical models Reviews User-generated content Taxonomy Cyberbullying

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An Efficient Non-standard Finite Difference Scheme for Solving Distributed Order Time Fractional Reaction–Diffusion Equation

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International Journal of applied and Computational Mathematics 2022年第2期8卷 1-17页

作者： Taghipour, M. Aminikhah, H. Department of Applied Mathematics and Computer Science Faculty of Mathematical Sciences University of Guilan P.O. Box 1914 Rasht 41938 Iran Center of Excellence for Mathematical Modelling Optimization and Combinational Computing (MMOCC) University of Guilan P.O. Box 1914 Rasht 41938 Iran

In this article, a nonstandard finite difference scheme is proposed for solving the distributed order time-fractional reaction–diffusion equation. In this equation, the time-fractional derivative is defined in the Caputo sense. We decompose the equation into two sub-equations and use the exact finite difference scheme with the Clenshaw–Curtis quadrature for sub-equations, then combine them to obtain the numerical method. The term involving the time distributed-order derivative is discretized using the Clenshaw–Curtis quadrature, and the Caputo fractional derivative is approximated by the Grünwald difference operator. We show that the presented scheme is uniquely solvable, unconditionally stable, and convergent. Numerical experiments are carried out to inspect the effectiveness of the presented scheme. © 2022, The Author(s), under exclusive licence to Springer Nature India Private Limited.

关键词： Caputo derivative Clenshaw–Curtis quadrature Distributed order fractional differential equation Non-standard finite difference scheme Reaction–diffusion equation Stability and convergence

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MOAC: Multi-Objective Adaptive Clustering Optimization Algorithm Based on Cluster Adaptation

MOAC: Multi-Objective Adaptive Clustering Optimization Algor...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Dheeb Albashish Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel Multi-Objective Adaptive Clustering (MOAC) algorithm that addresses current limitations in existing multi-objective optimization methods. While current algorithms often struggle with premature convergence and diversity preservation, the MOAC algorithm proposes a novel clustering-based approach to search space by partitioning. The algorithm combines cluster evolution with an adaptive parameter control mechanism to achieve significant improvements in balancing exploration-exploitation and diversity maintenance. MOAC employs k-means clustering for population partitioning, combined with cluster-based genetic operators, and an efficient diversity preservation scheme. Extensive experiments across different ZDT, MMF, and UF test suites demonstrate that MOAC's performance is superior, which achieves outstanding hypervolume and IGD values in complex multi-modal problems. The algorithm shows strength in handling complex Pareto fronts, and comprehensive statistical analysis through the Friedman rankings test with a 2.21 rank. Moreover. The results reveal that MOAC is a robust approach for complex multi-objective optimization problems, particularly maintaining solution diversity and achieving consistent convergence across different problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Clustering algorithms Optimization methods Evolutionary computation Partitioning algorithms Maintenance Convergence Genetic operators

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