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Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification

computers in Biology and Medicine 2025年 185卷 109593-109593页

作者： Khan, Zeshan Aslam Waqar, Muhammad Raja, Muhammad Junaid Ali Asif Chaudhary, Naveed Ishtiaq Khan, Abeer Tahir Mehmood Anwar Raja, Muhammad Asif Zahoor International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Rawal Institute of Health Sciences Islamabad Pakistan

Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard methods, especially in healthcare settings. Among the biggest threats to global public health is the fast spread of malaria. The plasmodium falciparum infection, the disease origin causes the intestinal illness. Fortunately, advances in artificial intelligence techniques have made it possible to use visual data sets to quickly and effectively diagnose malaria which has also proven to be cost and time effective. In literature, several DL approaches have previously been used with good precision but suffer from computational inefficiency and interpretability. Therefore, this research proposes a generalized fractional order-based explainable lightweight convolutional neural network model to overcome these limitations. The fractional order optimization algorithms have proven worth in terms of estimation accuracy and convergence speed for different applications. The proposed fractional order optimizer-based model offers an improved solution to malaria disease diagnosis with a percentage accuracy of 95 % using the standard NIH dataset and outperforms the existing complex models concerning speed and effectiveness. The proposed fractionally optimized lightweight CNN model has shown substantial performance on the external MP-IDB dataset and M5 test set as well by achieving a generalized test accuracy of 92 % and 90.4 % which verifies the robustness and generalizability of the proposed solution under available circumstances. Moreover, the efficacy of the proposed lightweight architecture is endorsed through evaluation metrics of precision, recall, and F1-score. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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AI-based Fuzzy Clustering System for Improving Customer Relationship Management 6

AI-based Fuzzy Clustering System for Improving Customer Rela...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Krishnareddy, Kr Aravinda, T.V. Nair, Kiran Patel, Umesh Kumar Sadvokasova, Gaukhar Shyamala Susan, V. Sjm Institute of Technology Department of Computer Science and Engineering India College Abu Dhabi School of Management. Department of Management United Arab Emirates Chaudhary Charan SIngh University Department of Bca Noida India International Education Corporation Department of Architecture Almaty050042 Kazakhstan A P C Mahalaxmi College for Women Department of Computer Science Tamilnadu Tuticorin India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Artificial Intelligence (AI) is a technology that is becoming more and more widely recognized with the addition of technologies such as personal helpers and chatbots. To get the most out of AI, your company needs to understand how to implement it better. Today, AI enhances almost every business segment and Customer Relationship Management (CRM), which is the area that has the most benefit in enhancing a better customer experience. The existing literature does not provide sufficient empirical evidence of how social media technologies affect a company's distribution chain relationships. Today, Artificial Intelligence empowers almost every business segment and CRM, providing the greatest benefit in enhancing a better customer experience. The main purpose of this study is to show how to successfully implement chatbot in CRM using AI-based Fuzzy Clustering system (AI-FCS), how to integrate chatbot with CRM and how chatbots affect customer relationship management. From forecast lead scoring to service chatbots to customized marketing operations. Chatbots can provide tools to provide a highly productive and better, personalized customer experience to all employees. Chatbots are now easily accessible to everyone in your organization, analyzing data, predicting and planning the next steps, and automating tasks and decisions. Create an optimal control model for Customer Relationship Management using the optimal control theory, depending on the characteristics of customer relationship management. With this model, it can gain effective management insights into Customer Relationship Management for optimal new customer acquisition and existing customer retention strategies. © 2022 IEEE.

关键词： Fuzzy clustering

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Anomaly Localization at High-Density Crowd Using Motion Shape Image (MSI) 1st

Anomaly Localization at High-Density Crowd Using Motion Shap...

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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Farooq, M.U. Mohamad Saad, M.N. Daud Khan, S. Saleh, Y. Universiti Teknologi PETRONAS Tronoh Malaysia Department of Computer Science National University of Technology Islamabad Pakistan Electrical Engineering Department Faculty of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia

ISBN: (纸本)9789811621826

Anomaly localization plays a critical role if a disaster occurs in a high-density crowd to efficiently rescue the crowd from the right location. This paper enriches anomaly localization by introducing new localization features, in addition to the ‘only’ localization feature of source/start point detection existing literature offers. New features introduced include crowd density estimation in localized regions and direction/angle of a localized region. A motion shape image (MSI) based approach is introduced for localization and features detection and experimentation is performed on benchmark and our proposed high-density crowd datasets. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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IoT Based Systems and Services: Recent Security Concerns and Feasible Solutions

IoT Based Systems and Services: Recent Security Concerns and...

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Humanitarian technology Conference (KHI-HTC), IEEE Karachi section

作者： Hafiz Muhammad Attaullah Shahzad Memon Ozlem Feyza Erkan Ruba Khawar Department of Computer Science Mohammad Ali Jinnah University Karachi Pakistan Department of Computer Engineering Faculty of Engineering and Architecture Beykoz University Istanbul Türkiye Faculty of Information Sciences and Humanities NED University of Engineering and Technology Karachi Pakistan

The term Internet of Things (IoT) is used to refer as embedded devices or objects with internet access, allowing them to communicate globally, interacting with people and networks. IoT security issues are directly related to the widespread use of its systems. This paper examines the security technologies employed in IoT and their respective features and applications. Additionally, it addresses the various security concerns associated with IoT-based systems and services, highlighting their occurrence within the three-layer framework of IoT. These days, as IoT introduces advanced application architectures that minimizes human intervention by integrating businesses in some way to address digital computer processes by communicating within a given network. Moreover, it will be discussed the key technologies associated with Advanced Encryption Standards (AES) and the changes brought in market value and usage of services that has been taken place b since the IoT incorporated.

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Vulnerability Assessment of Urban Road Network in Face of Ride-Hailing Malicious Dispatch Attack: A Case Study of Nanjing

SSRN

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SSRN 2023年

作者： Zhao, Na Tian, Yongshang Lu, Yuwen Zhai, Guofang School of Architecture and Urban Planning Nanjing University 210093 China Department of Computer Science and Technology Nanjing University 210023 China

As a typical ICT-based transportation infrastructure in smart cities, ride-sharing platforms significantly enhance the efficiency of urban public transportation. However, they face severe cybersecurity risks. Cities like Moscow have experienced incidents where hackers infiltrated ride-sharing platforms to maliciously dispatch vehicles, leading to extensive traffic congestion. Existing literature has paid much attention to road network vulnerability against natural disasters. Yet, less focus has been put on disturbances due to such cyber attacks. In this study, we assess the vulnerability of the downtown road network in Nanjing during the morning peak hours when subjected to ride-hailing malicious dispatch attacks. We begin by adopting the perspective of attackers, leveraging relevant theories from graph theory to devise an optimized malicious vehicle dispatch scheme under the real-time road conditions. Subsequently, we conduct simulated attacks against the target network in five major commuting directions. Results show that this attack can significantly decrease the road traffic capacity in some critical directions with just a dispatch of tens of vehicles. Additionally, there is a noticeable disparity in vulnerability across different directions. In response, we further identify the primary factors contributing to these variations, which aims to enhance the resilience of urban road network in the information age. © 2023, The Authors. All rights reserved.

关键词： Traffic congestion

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Deep Learning Approach for Divergence Behavior Detection at High Density Crowd 1st

Deep Learning Approach for Divergence Behavior Detection at ...

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1st International Conference on Artificial Intelligence for Smart Community, AISC 2020

作者： Farooq, M.U. Mohamad Saad, M.N. Saleh, Y. Daud Khan, S. Universiti Teknologi PETRONAS Tronoh Malaysia Electrical Engineering Department Faculty of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science National University of Technology Islamabad Pakistan

ISBN: (纸本)9789811621826

At high-density crowd gatherings, people naturally escape from the region where any unexpected event happens. Escape in high-density crowds appears as a divergence pattern in the scene and timely detecting divergence patterns can save many human lives. In this paper, we propose to physically capture crowd normal and divergence motion patterns (or motion shapes) in form of images and train a shallow convolution neural network (CNN) on motion shape images for divergence behavior detection. Crowd motion pattern shape is obtained by extracting ridges of Lagrangian Coherent Structure (LCS) from the Finite-Time Lyapunov Exponent (FTLE) field and convert ridges into the grey-scale image. We also propose a divergence localization algorithm to pinpoint anomaly location(s). Experimentation is carried out on synthetic crowd datasets simulating normal and divergence behaviors at the high-density crowd. Comparison with state-of-the-art methods shows our method can obtain better accuracy for both divergence behavior detection and localization problems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Motion estimation

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The Importance of Being Earnest: Multiple Heterogeneous Container Loading with a Simple Genetic Algorithm 28th

The Importance of Being Earnest: Multiple Heterogeneous Con...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Rusin, Francesco Fiala, Jan Sanker, Julian Bhattarai, Suyesh Ekárt, Anikó Department of Engineering and Architecture University of Trieste Trieste Italy Institute of Automation and Computer Science Brno University of Technology Brno Czech Republic Yale College Yale University New Haven United States Mechatherm International Limited Cradley Heath United Kingdom Aston Centre for Artificial Intelligence Research and Application Aston University Birmingham United Kingdom

ISBN: (纸本)9783031900617

In this study we address the complex practical problem of multiple heterogeneous container loading with a simple genetic algorithm. We demonstrate that with a well-chosen representation including a heuristic and a suitable fitness function the other aspects of the genetic algorithm do not need extensive work for good results. Following systematic study of our method on synthetically generated data, we visually showcase the solution for a company-based problem instance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Genetic algorithms

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Novel polynomial Abet data augmentation algorithm with GRU paradigm for nuclear power prediction

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Annals of Nuclear Energy 2024年 201卷

作者： Khan, Saifullah Awan, Saeed Ehsan Muhammad, Yasir Jadoon, Ihtesham Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 0.3 Yunlin Douliou64002 Taiwan

In current study innovative approach is presented to improve the accuracy and efficiency of machine learning models on the dataset for each of the 31 US states, solely comprising a single power output column in Terra watt-hours (TWh) spanning from January 2013 to November 2020 by exploiting the knacks of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), hybrid GRU-LSTM, Polynomial Feature Transform (PFT) and Polynomial Abet (PA) data augmentation with GRU for efficacious prediction. Achieving sufficient accuracy, a small univariate dataset of 95 rows was augmented through the PFT method, resulting in a significant reduction in prediction errors. Encouraged by this improvement, a novel algorithm PA data augmentation with GRU is introduced for further enhancing the accuracy, correlation, and computational efficiency of the prediction model. This research not only demonstrates the superior performance of the proposed PA data augmentation method but also highlights the benefit of using 31 individual univariate datasets for each state, allowing comprehensive testing and validation of the novel algorithm, providing compelling evidence of its efficacy in nuclear power prediction. The results from the proposed algorithms are evaluated using Root Mean Square Error (RMSE) and Relative Root Mean Square Error (RRMSE). A quantitative overview of PA and PFT data augmentation techniques’ comparison show the result in terms of accuracy (PA's 0.464% RRMSE versus PFT's 1.088 % RRMSE), correlation (PA's −0.5877 versus PFT's 0.4892), and computational efficiency (PA's 14.02 % less computational time then PFT) for the nuclear power prediction model. The research framework introduced in this study, with its focus on utilizing state-specific data for nuclear energy prediction, holds significant implications for governmental decision-making policies. It provides a comprehensive overview of the trends in power generation and can be utilized for both renewable and thermal sources ac

关键词： Long short-term memory

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Unsupervised Fouling Reconstruction in the Pipe Bend

Unsupervised Fouling Reconstruction in the Pipe Bend

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IEEE Symposium (IUS) Ultrasonics

作者： Denys Iablonskyi Carlos-Omar Rasgado-Moreno Madis Ratassepp Arto Klami Edward Hæggström Ari Salmi Department of Computer Science Department of Physics University of Helsinki Helsinki Finland Department of Civil Engineering and Architecture Tallinn University of Technology Tallinn Estonia Department of Computer Science University of Helsinki Helsinki Finland Department of Physics University of Helsinki Helsinki Finland

Guided wave tomography allows the investigation of extended areas with a limited number of measurements, typically using transducer arrays. Most imaging methods rely heavily on the material properties such as dispersion curves, that in the case of fouling deposition are not always known. In the case of complex shaped structures, geometrical anisotropy can bring additional complexity. Here we present an unsupervised machine learning approach based on the Gaussian process to detect and characterize the fouling in a pipe bend that relies only on the difference between clean/healthy and fouled/damaged measured signals.

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A computational supervised neural network procedure for the fractional SIQ mathematical model

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European Physical Journal: Special Topics 2023年第5期232卷 535-546页

作者： Mukdasai, Kanit Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Singkibud, Peerapongpat Sadat, R. Ali, Mohamed R. Department of Mathematics Faculty of Science Khon Kaen University Khon Kaen 40002 Thailand Department of Mathematics and Statistics Hazara University Mansehra Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou 64002 Taiwan Department of Applied Mathematics and Statistics Faculty of Science and Liberal Arts Rajamangala University of Technology Isan Nakhon Ratchasima 30000 Thailand Department of Mathematics Faculty of Engineering Zagazig University Zagazig Egypt Faculty of Engineering and Technology Future University in Egypt New Cairo 11835 Egypt Basic Engineering Science Department Benha Faculty of Engineering Benha University Benha Egypt Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. © 2022, The Author(s), under exclusive licence to EDP sciences, Springer-Verlag GmbH Germany, part of Springer Nature.

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