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Does revealed comparative advantage matter in the gravity of FTAs?

Journal of International Logistics and Trade 2023年第2期21卷 84-107页

作者： Ahmed, Arsalan Nazeer, Nazia Zahid, GulRukh Nawaz, Faisal Business School University of Southampton Malaysia Campus Iskandar Puteri Malaysia National University of Computer and Emerging Sciences Lahore Pakistan Applied Economic Research Centre University of Karachi Karachi Pakistan Department of Mathematics Dawood University of Engineering and Technology Karachi Pakistan

Purpose - This study attempts to recognize the effects of the Pakistan-China free trade agreements (PCFTA) on promoting trade between the two economies. Design/methodology/approach - Following the concept of revealed comparative advantage (RCA) and free trade agreements, the study first identifies those commodities in which Pakistan and China have a robust RCA and then analyze the effect of PCFTA on the export value of those commodities for the bilateral trade between Pakistan and China. The study used the panel data in which more than the top 150 importers (j) have been selected for each case of Pakistan and China for the period of 2003-2015. Findings - The study concludes that even with the higher convergence rate, the good RCA does not guarantee a positive effect of the free trade agreement on the commodities. Originality/value - The study contributes to the existing literature by integrating RCA with the gravity model by adopting a sequential mode for Pakistan-China free trade agreement. © 2023 The Author(s).

关键词： Gravity model Pakistan-China free trade agreements Revealed comparative advantage Trade liberalization

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

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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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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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computer Modeling in engineering & Sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

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Intelligent Automation & Soft Computing 2023年第8期37卷 1275-1291页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation *** classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS ***,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual *** ability to extract important features is vital in making RST classification more *** work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction *** used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification *** experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art *** proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt ***,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

关键词： Road surface type classification deep learning inertial sensor deep pyramidal residual network squeeze-and-excitation module

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Pre-Impact and Impact Fall Detection Based on a Multimodal Sensor Using a Deep Residual Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 3371-3385页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Falls are the contributing factor to both fatal and nonfatal injuries in the ***,pre-impact fall detection,which identifies a fall before the body collides with the floor,would be ***,researchers have turned their attention from post-impact fall detection to pre-impact fall ***-impact fall detection solutions typically use either a threshold-based or machine learning-based approach,although the threshold value would be difficult to accu-rately determine in threshold-based ***,while additional features could sometimes assist in categorizing falls and non-falls more precisely,the esti-mated determination of the significant features would be too time-intensive,thus using a significant portion of the algorithm’s operating *** this work,we developed a deep residual network with aggregation transformation called FDSNeXt for a pre-impact fall detection approach employing wearable inertial *** proposed network was introduced to address the limitations of fea-ture extraction,threshold definition,and algorithm *** training on a large-scale motion dataset,the KFall dataset,and straightforward evaluation with standard metrics,the proposed approach identified pre-impact and impact falls with high accuracy of 91.87 and 92.52%,*** addition,we have inves-tigated fall detection’s performances of three state-of-the-art deep learning models such as a convolutional neural network(CNN),a long short-term memory neural network(LSTM),and a hybrid model(CNN-LSTM).The experimental results showed that the proposed FDSNeXt model outperformed these deep learning models(CNN,LSTM,and CNN-LSTM)with significant improvements.

关键词： Pre-impact fall detection deep learning wearable sensor deep residual network

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Intelligent recognition system for viewpoint variations on gait and speech using CNN-CapsNet

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 363-382页

作者： G.Merlin Linda N.V.S.Sree Rathna Lakshmi N.Senthil Murugan Rajendra Prasad Mahapatra V.Muthukumaran M.Sivaram Department of Computer Science and Engineering SRM Institute of Science and Technology–Vadapalani CampusChennaiIndia Department of Electronics and Communication Engineering Agni College of TechnologyChennaiIndia School of Computer Science and Engineering VIT-AP UniversityAmaravatiIndia Department of Computer Science and Engineering SRM Institute of Science and TechnologyGhaziabadIndia Department of Mathematics School of Applied SciencesREVA UniversityBangaloreIndia Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam

Purpose-The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and *** proposes a convolutional neural network-based capsule network(CNN-CapsNet)model and outlining the performance of the system in recognition of gait and speech *** proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech ***/methodology/approach-This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNNand used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in *** proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of ***-This research work provides recognition of signal,biometric-based gait recognition and sound/speech *** evaluations are conducted on three aspects of scenarios,namely fixed-view,cross-view and multi-view *** main parameters for recognition of gait are speed,change in clothes,subjects walking with carrying object and intensity of *** limitations/implications-The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware *** implications-This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware ***/value-This proposed research work proves to be performing better for the recognition of gait and speech when

关键词： Intelligent system Gait recognition Convolutional neural network Deep learning Capsule network Viewpoint variations

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An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

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Intelligent Automation & Soft Computing 2023年第1期35卷 1245-1259页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok 10800Thailand

Smoking is a major cause of cancer,heart disease and other afflictions that lead to early *** effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment *** activities often accompany other activities such as drinking or ***,smoking activity recognition can be a challenging topic in human activity recognition(HAR).A deep learning framework for smoking activity recognition(SAR)employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules(ResNetSE)to increase the effectiveness of the SAR *** proposed model was tested against basic convolutional neural networks(CNNs)and recurrent neural networks(LSTM,BiLSTM,GRU and BiGRU)to recognize smoking and other similar activities such as drinking,eating and walking using the UT-Smoke *** different scenarios were investigated for their recognition performances using standard HAR metrics(accuracy,F1-score and the area under the ROC curve).Our proposed ResNetSE outperformed the other basic deep learning networks,with maximum accuracy of 98.63%.

关键词： Smoking activity recognition deep residual network smartwatch sensors deep learning

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IoT Malware Detection Tool with CNN Classification for Small Devices 6

IoT Malware Detection Tool with CNN Classification for Small...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Bhatnagar, Gunjan Kumar Rajoria, Yogendra Sakeel, Md. Vigenesh, M. Premananthan, G. Dongre, Deepika School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India School of Basic & Applied Sciences K. R. Mangalam University Department of Mathematics Gurugram India Invertis University Department of Computer Applications Bareilly India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India Vishwakarma Institute of Information Technology AI & Ds Pune India

ISBN: (纸本)9798350304480

Since the number of linked devices continues to grow quickly, the Web of Everything (IoT) is being employed change today in more industries. More issues in regards to safety, stability, and supportability are brought on by an increase in smart devices, particularly as it relates to 5G technology. We are concentrating on the safety component of IoT systems in just this article because it is a relatively new field. For varied purposes, such as cost savings or a desire to forego the use of generation system, many Internet device manufacturers need not take security into account when designing their products. The opponent may use those potentially dangerous technologies to carry out several destructive assaults. As a result, we created a device capable of recognizing malicious activity from a particular IoT networks node. They are capable to offer detection methods for IoT utilizing a primary key that may be put inside the system using a multilayer brain networks for identification and surveillance. The accomplishment dispels any misconceptions about methods for deep training while demonstrating how that systems can be simply adapted and deployed to every system. © 2023 IEEE.

关键词： Classification CNN IoT Machine Learning Malware Multicategories

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MotionNeXt: A Deep Learning Neural Network for Recognizing Human Motions Related Activities Using Inertial Sensors 23

MotionNeXt: A Deep Learning Neural Network for Recognizing H...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350353952

Recognition of human activities using wearable sensors is essential for applications related to health and well-being. However, the complex data captured by inertial sensors poses challenges in accurately identifying actions. This research introduces MotionNeXt, a residual deep neural network incorporating aggregated transformation. MotionNeXt utilizes residual and multi-kernel blocks to extract spatial and temporal characteristics from raw IMU data. Subsequently, it employs global average pooling, a fully connected layer, and softmax for classification purposes. An attention layer enables the model to focus on specific segments of the input sequence while categorizing each time step. MotionNeXt is evaluated using a publicly available dataset of activities collected from wearable inertial measurement devices. It achieves state-of-the-art accuracy, boasting an F1-score of 99.37%, surpassing previous deep learning methods by 2- 5%. By integrating accelerometer, gyroscope, and magnetometer modalities, MotionNeXt mitigates the limitations of individual inputs, significantly improving accuracy across various models. © 2024 IEEE.

关键词： Deep neural networks

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Hyperstability of cubic functional equation in banach space

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Annali dell'Universita di Ferrara 2023年第2期69卷 317-328页

作者： Aribou, Youssef Dimou, Hajira Rossafi, Mohamed Laboratory Mathematical and Computer Engineering (IMI) Laayoune Higher School of Technology Ibn Zohr University Laayoune Morocco Department of Mathematics Faculty of Sciences Applied Ibn Zohr University Agadir Morocco LaSMA Laboratory Department of Mathematics Faculty of Sciences Dhar El Mahraz University Sidi Mohamed Ben Abdellah Fes Morocco

In this paper, we prove some hyperstability results of the following cubic functional equation f(2x+y)+f(2x-y)=2f(x+y)+2f(x-y)+12f(x)on a restricted domain. © 2022, The Author(s) under exclusive license to Univer... 详细信息

关键词： Banach space Cubic functional equation Hyperstability

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