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Divide-and-train: A new approach to improve the predictive tasks of bike-sharing systems

mathematical Biosciences and engineering 2024年第7期21卷 6471-6492页

作者： Ali, Ahmed Salah, Ahmad Bekhit, Mahmoud Fathalla, Ahmed Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044 Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ad-Dhahirah Ibri Oman Zagazig University Department of Computer Science Faculty of Computers and Informatics Sharkeya Zagazig Egypt Sydney Australia Kaplan Business School Sydney Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia Egypt

Bike-sharing systems (BSSs) have become commonplace in most cities worldwide as an important part of many smart cities. These systems generate a continuous amount of large data volumes. The effectiveness of these BSS systems depends on making decisions at the proper time. Thus, there is a vital need to build predictive models on the BSS data for the sake of improving the process of decision-making. The overwhelming majority of BSS users register before utilizing the service. Thus, several BSSs have prior knowledge of the user’s data, such as age, gender, and other relevant details. Several machine learning and deep learning models, for instance, are used to predict urban flows, trip duration, and other factors. The standard practice for these models is to train on the entire dataset to build a predictive model, whereas the biking patterns of various users are intuitively distinct. For instance, the user’s age influences the duration of a trip. This endeavor was motivated by the existence of distinct user patterns. In this work, we proposed divide-and-train, a new method for training predictive models on station-based BSS datasets by dividing the original datasets on the values of a given dataset attribute. Then, the proposed method was validated on different machine learning and deep learning models. All employed models were trained on both the complete and split datasets. The enhancements made to the evaluation metric were then reported. Results demonstrated that the proposed method outperformed the conventional training approach. Specifically, the root mean squared error (RMSE) and mean absolute error (MAE) metrics have shown improvements in both trip duration and distance prediction, with an average accuracy of 85% across the divided sub-datasets for the best performing model, i.e., random forest. © 2024 the Author(s).

关键词： Forecasting

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Enhancing Gastrointestinal Image Classification: A Fusion of CNN and Vision Trasformers 2

Enhancing Gastrointestinal Image Classification: A Fusion of...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Ganesh, Bachu Annamalai, R. Jayapal, Senthil Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Collage of Computing and Information Science University of Technology and Applied Sciences Ibra Oman

ISBN: (纸本)9798350383652

In order to meet the pressing demand for early diagnosis in healthcare, we proposed a unique hybrid architecture in this study that is intended for the categorization of gastrointestinal (GI) illnesses. With fewer trainable parameters, faster training times, and less storage needed for model weights, our suggested architecture prioritizes efficiency achieves an impressive 94% accuracy. To effectively diagnose GI disorders, this design combines convolutional neural networks (CNNs) with Transformers. The creation of this hybrid architecture, which boasts excellent accuracy in illness categorization, decreased storage requirements, and efficiency in training, is one of the research's accomplishments. Our proposed model demonstrates its flexibility for many uses, encouraging simple implementation and expandability. Extensive tests performed on a large dataset of endoscopic images highlight our hybrid architecture's better performance over current techniques. With this study, diagnostics for gastrointestinal diseases have advanced significantly, opening the door to future diagnostic systems that will be more precise and effective. The proposed architecture has outperformed the traditional pre-trained models both in classification accuracy and time to train the model with no weights. © 2024 IEEE.

关键词： Endoscopy

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Analyzing the function of smart security in the context of the internet of things 3

Analyzing the function of smart security in the context of t...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naidu, K. Venkatagurunatham Pillai, Anchu Krishnan Rajaprabha, C.E. Sharma R, Rajesh Ul Islam, Atowar Khasimbee, Shaik Dr M.G.R Educational and Research Institute Dept. of Computer Science and Engineering Chennai India Xavier Institute of Engineering Applied Science and Humanities Maharashtra Mumbai India Hindusthan Institute of Technology Department of Computer Science and Engineering Coimbatore India Adama Science and Technology University School of Electrical Engineering and Computing Department of Computer Science and Engineering Nazareth1888 Ethiopia University of Science and Technology Meghalaya Department of Computer Science and Electronics Meghalaya Baridua793101 India Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India

ISBN: (纸本)9798350399264

Technology is getting better day by day and human beings are using the latest technology possible for their needs and safety. Many devices have been invented that use the internet and with this human safety issues are also taken care of up to a great extent. GPS tracking devices, SHE (Society Harnessing Equipment), AESHS (Advanced Electronic System for Human Safety), ILA Systems are one of the most useful applications and devices that are used for human safety. Human safety and especially the safety of girls have become an emerging and sensitive issue in the present world scenario. This article deals with the role of the Internet of Things in advancing smart security. The issues are taken care of with the help of these upgraded devices that are easily accessible from a remote location with an internet connection. © 2023 IEEE.

关键词： Internet of things

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Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images

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computers, Materials & Continua 2022年第12期73卷 5735-5753页

作者： Abdullah A.Asiri Muhammad Aamir Ahmad Shaf Tariq Ali Muhammad Zeeshan Muhammad Irfan Khalaf A.Alshamrani Hassan A.Alshamrani Fawaz F.Alqahtani Ali H.D.Alshehri Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran 61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Computer Science Bahauddin Zakariya UniversityMultan 66000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor *** brain tumor segmentation for cancer analysis from many Magnetic Resonance Images(MRIs)created in medical practice is a problematic and timewasting task for *** a result,there is a critical necessity for more accurate computeraided methods for early tumor *** remove this gap,we enhanced the computational power of a computer-aided system by proposing a finetuned Block-Wise Visual Geometry Group19(BW-VGG19)*** this method,a pre-trained VGG19 is fine-tuned with CNN architecture in the block-wise mechanism to enhance the system`s *** publicly accessible Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)dataset collected from 2005 to 2020 from different hospitals in China has been used in this *** proposed method is simple and achieved an accuracy of 0.98%.We compare our technique results with the existing Convolutional Neural network(CNN),VGG16,and VGG19 *** results indicate that our proposed technique outperforms the best results associated with the existing methods.

关键词： CNN brain tumor block-wise structure VGG19 VGG16

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A robust multi-objective optimization algorithm for accurate parameter estimation for solar cell models

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Soft Computing 2024年第19期28卷 11265-11277页

作者： Singla, Manish Kumar Gupta, Jyoti Alsharif, Mohammed H. Kim, Mun-Kyeom Aljaidi, Mohammad Safaraliev, Murodbek Department of Interdisciplinary Courses in Engineering Chitkara University Institute of Engineering & ampTechnology Chitkara University Punjab India Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology K. R. Mangalam University Gurgaon Haryana122003 India Department of Electrical Engineering College of Electronics and Information Engineering Sejong University 209 Neungdong-Ro Gwangjin-Gu Seoul05006 Korea Republic of School of Energy System Engineering Chung-Ang University 84 Heukseok-Ro Dongjak-Gu Seoul06974 Korea Republic of Department of Computer Science Zarqa University Zarqa13110 Jordan Department of Automated Electrical Systems Ural Federal University Yekaterinburg620002 Russia

The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality. © The Author(s) 2024.

关键词： Solar cells

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Dynamic Bidirectional Translation for Sign Language by Using Machine Learning-Infused Approach with Integrated computer Vision 2

Dynamic Bidirectional Translation for Sign Language by Using...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jain, Somya Diwakar, Shikha Yadav, Neha G.L. Bajaj Institute of Technology and Management Engineering/ Computer Science Artificial Intelligence Department of Applied Computational Science India School of Information & Communication Technology Gautam Buddha University Engineering/Artificial Intelligence India G.L. Bajaj Institute of Technology and Management Department of Applied Computational Science India

ISBN: (纸本)9798350371055

This research aims to break communication barriers for the deaf and hard-of-hearing by pioneering a real-time, dynamic sign language translator. Unlike existing apps, it uses a CNN to translate simultaneously between English and American Sign Language (ASL). It features an animated avatar for interactive communication, and goes beyond translation with chat, video calls, and precise hand motion capture. This innovative approach paves the way for inclusive and secure communication within the deaf community. © 2024 IEEE.

关键词： ASL avatar CNN communication barriers deaf inclusive real-time Sign language translation

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Impact of Gamification on Student Learning: An Empirical Evidence 5th

Impact of Gamification on Student Learning: An Empirical Evi...

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5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Garg, Anchal Madhulika Centre for Applied Computer Science School of Creative Technologies University of Bolton BoltonBL3 5AB United Kingdom Department of Computer Science and Engineering Amity School of Engineering and Technology Uttar Pradesh Noida India

ISBN: (纸本)9789819716814

Learner diversity is a matter for universities enrolling international students. Hence, learner engagement then becomes a major concern for instructors. This study uses gamification techniques to determine its impact on engagement and learning when it comes to diverse cohort of learners. A survey questionnaire method was used to identify the effect of gamification on engagement, motivation, performance, and learning. This empirical study revealed that quizzes supported by gamification enhanced engagement, performance, and learning but did not motivate learners to study outside the lecture theatre. This paper also summarizes the issues while using gamified quizzes in the classroom settings and provides potential solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Back propagation artificial neural network for diagnose of the heart disease

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Journal of Reliable Intelligent Environments 2023年第1期9卷 57-85页

作者： Kaur, Jagmohan Khehra, Baljit S. Singh, Amarinder IKG Punjab Technical University Punjab Jalandhar144103 India Computer Science and Engineering BBSB Engineering College Punjab Fatehgarh Sahib140407 India Department of Applied Sciences BBSB Engineering College Punjab Fatehgarh Sahib140407 India

Nowadays, coronary heart disease is one of the most fatal disease globally. Many researchers and medical technicians have developed and designed various computer-aided diagnosis systems using various machine learning models such as random forest, linear regression, rough set, naive bayes, artificial neural network, support vector machine, multivariate adaptive regression splines, K-nearest neighbor and decision tree to name a few. This era of digital health domain demands early prediction of heart disease which is the crucial need to control the global mortality rate of this particular disease. Commonly used methods for heart disease detection are clinical and expert dependant which makes them costly and inaccessible to the masses. In this paper, an efficient-cum-automated coronary heart disease diagnosis model is being proposed using multi-layered artificial neural network with back propagation algorithm. The proposed model compares the variation caused by different number of neurons used in the hidden layers for different transfer functions. The model has been implemented on Kaggle and Statlog heart disease dataset with thirteen clinical parameters. The experimental results attained an accuracy of 99.92% using six hidden layers with tan-hyperbolic transfer function. The results have been substantiated through statistical parameters and k-fold cross validation. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Nearest neighbor search

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Advancing Industrial Cybersecurity: Machine Learning-Based Detection and Mitigation of IIoT Attacks 5

Advancing Industrial Cybersecurity: Machine Learning-Based D...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kumar, S Gouri Kiran Prasad, Raavi Satya College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman Dhanekula Institute of Engineering & Technology Department of Computer Science & Engineering A.P. Vijayawada India

ISBN: (纸本)9798331518097

A threatening entry or exploitation of vulnerabilities within an industrial network or system connected to IoT devices is an IIoT (Industrial Internet of Things) attack. Due to manufacturing delays, broken equipment, lost confidential data, and fines from the government, cyberattacks on IIoT systems can cause significant financial losses. Rapid reaction and recovery are made possible by efficient detection systems, which can lessen these losses. Many researchers have applied machine learning (ML) approaches to predict IIoT attacks in advance. Industrial networks can be highly effectively and dynamically protected against various cyber threats by utilizing MLthrough IIoT attack distinguishing. This ML methodology is a vital component of today's cybersecurity toolkit for IIoT contexts owing to its capacity for handling massive info, adjusting for novel threats, & delivering insights in real-time. Industrial networks can be highly effectively and dynamically protected against cyber threats by utilizing ML to IIoT attack identification. ML is a vital component of today's cybersecurity toolkit for IIoT contexts for its capacity to handle massive quantities of info, adjust to emerging risks, & deliver insights in real time. IIoT data can contain noise, missing values, or irrelevant information, negatively affecting model performance. Identifying and extracting relevant features from IIoT data was highly complex and time-consuming. The methodology's effectiveness is heavily related to features with high quality utilized. This paper discusses the various techniques, their limitations, and the gaps studied during the literature survey. Among the machine learning approaches, the model should adapt to the new infrastructure attacks, which is effectively possible only through meta-heuristic learning algorithms. © 2024 IEEE.

关键词： Cyber attacks

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Deep Learning Approach for COVID-19 Detection in Computed Tomography Images

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computers, Materials & Continua 2021年第5期67卷 2093-2110页

作者： Mohamad Mahmoud Al Rahhal Yakoub Bazi Rami M.Jomaa Mansour Zuair Naif Al Ajlan Department of Applied Computer Science College of Applied Computer EngineeringKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Engineering College of Computer and Information ScienceKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information ScienceKing Saud UniversityRiyadh11362Saudi Arabia

With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is *** routine real-time reverse transcription-polymerase chain reaction(rRT-PCR)test that is currently used does not provide such high accuracy or speed in the screening *** the good choices for an accurate and fast test to screen COVID-19 are deep learning *** this study,a new convolutional neural network(CNN)framework for COVID-19 detection using computed tomography(CT)images is *** EfficientNet architecture is applied as the backbone structure of the proposed network,in which feature maps with different scales are extracted from the input CT scan *** addition,atrous convolution at different rates is applied to these multi-scale feature maps to generate denser features,which facilitates in obtaining COVID-19 findings in CT scan *** proposed framework is also evaluated in this study using a public CT dataset containing 2482 CT scan images from patients of both classes(i.e.,COVID-19 and non-COVID-19).To augment the dataset using additional training examples,adversarial examples generation is *** proposed system validates its superiority over the state-of-the-art methods with values exceeding 99.10%in terms of several metrics,such as accuracy,precision,recall,and *** proposed system also exhibits good robustness,when it is trained using a small portion of data(20%),with an accuracy of 96.16%.

关键词： COVID-19 deep learning computed tomography multi-scale features atrous convolution adversarial examples

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