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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao56000 Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok10800 Thailand

Human activity recognition (HAR) and user identification utilizing smartphone sensors are crucial in domains such as wellness tracking, tailored services, and security. Conventional approaches handle these activities as distinct entities, leading to less-Than-ideal performance and restricted applicability. This research presents a novel deep multi-Task learning network that simultaneously performs activity recognition and user identification using smartphone sensor data. By leveraging shared representations and task-specific information, the network enhances accuracy. It combines a shared convolutional neural network (CNN) with bidirectional long short-Term memory (BiLSTM) layers to extract features from raw sensor data. The CNN identifies patterns, while the BiLSTM captures sequential dependencies and contextual information. These features are processed by fully connected layers for both activity recognition and user identification. A multi-Task loss function is proposed to optimize both tasks based on their complexity and importance. The approach is tested on the UCI-HAR dataset, containing diverse activities from multiple users. Experimental results show that the proposed CNN-BiLSTM model achieves 97.64% accuracy in activity recognition and 82.59% in user identification, surpassing current state-of-The-Art methods. This demonstrates the effectiveness of the multi-Task learning framework and the advantage of using the shared CNN-BiLSTM architecture for improving both tasks. © 2025 The Author(s).

关键词： Convolutional neural networks

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Optimal Deep Dense Convolutional Neural Network Based Classification Model for COVID-19 Disease

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computers, Materials & Continua 2022年第1期70卷 2031-2047页

作者： A.Sheryl Oliver P.Suresh A.Mohanarathinam Seifedine Kadry Orawit Thinnukool Department of Computer Science and Engineering St.Joseph’s College of Engineering600119ChennaiIndia Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Faculty of Engineering Karpagam Academy of Higher EducationCoimbatore641021India Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and ***,these methods suffer from biased results and inaccurate detection of the ***,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT *** proposed methodology works on the basis of two stages such as pre-processing and ***,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT *** collected images are then preprocessed using Gaussian *** filter can be utilized in the removal of unwanted noise from the collected CT scan ***,the preprocessed images are sent to prediction *** this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed *** proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed ***,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or *** projected method was implemented in MATLAB and the performances were evaluated through statistical *** proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)*** results established the supremacy of the proposed model.

关键词： Deep learning deep dense convolutional neural network covid-19 CT images chimp optimization algorithm

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LowResIR-Net: Lightweight Residual Network for ADL Recognition Using Low-Resolution Infrared Sensor Data 16

LowResIR-Net: Lightweight Residual Network for ADL Recogniti...

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Detecting activities of daily living (ADL) is crucial for supported living and medical monitoring. Traditional approaches rely on high-resolution sensors and computationally intensive algorithms, limiting their scalability and practical use in real-world settings. This research introduces LowResIR-Net, a compact deep residual network architecture specifically tailored for recognizing ADL utilizing low-resolution infrared sensor data. LowResIR-Net efficiently extracts key features from low-dimensional data by leveraging the advantages of infrared sensing, such as privacy protection and light resilience. Its architecture combines residual connections with depth-wise convolutions, enhancing feature learning while maintaining a compact model size. The model’s performance is evaluated using the Coventry-2018 dataset, containing low-resolution infrared data from three residential sensor locations. Our tests have shown that in all three cases, LowResIR-Net consistently achieves superior performance compared to other cutting-edge deep learning models, such as CNN, LSTM, BiLSTM, GRU, and BiGRU. LowResIR-Net attains the most excellent accuracies of 90.18%, 93.75%, and 93.30%, and F1-scores of 81.81%, 89.55%, and 88.92% for scenarios I, II, and III, respectively. Furthermore, it achieves these results with fewer parameters than most other models. LowResIR-Net’s strong performance highlights its ability to accurately capture complex patterns in low-resolution infrared data for ADL detection. This study demonstrates the potential of lightweight deep learning and low-resolution infrared sensors in developing privacy-focused and computationally efficient wellness monitoring systems. © 2025 The Author(s).

关键词： Infrared detectors

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An Efficient Identity Based Multi- Proxy Multi-Signcryption Scheme for Electronic Commerce using Bilinear Pairing

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Procedia computer science 2025年 259卷 1592-1601页

作者： Tej Singh Rashid Ali Vikas Tyagi Department of Applied Sciences ABES Engineering College Ghaziabad UP India Department of Applied Sciences KIET Group of Institutions Ghaziabad UP India School of Computer Science Engineering & Technology Bennett University Greater Noida UP India

With the rapid growth of electronic commerce and the increasing need for secure communication in online transactions, there is a pressing demand for efficient and secure cryptographic schemes. The researcher is using various approaches to secure communication in online transactions. However, ensuring secure communication and protecting sensitive information in electronic commerce (E-commerce) systems remains a significant concern. Traditional cryptographic schemes often suffer from limitations in terms of efficiency, scalability, and flexibility. Thus, the proposed work presents an innovative Identity Based Multi-Proxy Multi-Signcryption (IB-MPMS) scheme for electronic commerce applications. It involves the collaboration of two distinct groups of signcrypters namely original signcryption and group of proxy signcrypters. To facilitate this process in the domain of secure and efficient communication in electronic commerce, we design a multi-proxy multi-signcryption scheme with the help of the bilinear pairings. This authorization enables the generation of a multi-proxy multi-signcryption in the proposed IB-MPMS scheme. Moreover, the collaborative effort of all signcrypters in the proxy group is considered towards the implementation of these multi-proxy multi-signcryption approaches. Due to the utilization of identity-based cryptography, the proposed scheme IB-MPMS effectively addresses the key management problem, resulting in simplified processes. However, in the proposed IB-MPMS scheme both the original and proxy signcrypter ensures fair protection during E-commerce transactions. Moreover, in comparison to previous schemes, our proposed scheme exhibits more efficiency. From a computational perspective, our scheme only necessitates 4 pairing computations, whereas the state of art requires 8 pairing computations.

关键词： Identity-based cryptography bilinear pairings multi-signature ID-based signcryption proxy-signature

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Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand 10

Negative Binomial Regression Model for Predicting the Number...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Thepsumritporn, Sthaporn Jitpattanakul, Anuchit Mekruksavanich, Sakorn Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

This article examines the use of count data models to predict the number of tourists visiting Thailand's national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models - Poisson (POI), and Negative Binomial (NB) - were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike Information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data's overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model's utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data. © 2025 IEEE.

关键词： Logistic regression

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ReForm-Eval: Evaluating Large Vision Language Models via Unified Re-Formulation of Task-Oriented Benchmarks 24

ReForm-Eval: Evaluating Large Vision Language Models via Uni...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Zejun Wang, Ye Du, Mengfei Liu, Qingwen Wu, Binhao Zhang, Jiwen Zhou, Chengxing Fan, Zhihao Fu, Jie Chen, Jingjing Wei, Zhongyu Huang, Xuanjing School of Data Science Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Software College Northeastern University Liaoning Shenyang China Alibaba Group Zhejiang Hangzhou China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9798400706868

Recent years have witnessed remarkable progress in the development of large vision-language models (LVLMs). Benefiting from the strong language backbones and efficient cross-modal alignment strategies, LVLMs exhibit surprising capabilities to perceive visual signals and perform visually grounded reasoning. However, the capabilities of LVLMs have not been comprehensively and quantitatively evaluated. Most existing multi-modal benchmarks require task-oriented input-output formats, posing great challenges to automatically assess the free-form text output of LVLMs. To effectively leverage the annotations available and reduce the manual efforts required for constructing new benchmarks, we propose to re-formulate existing benchmarks into unified LVLM-compatible formats. Through systematic data collection and reformulation, we present ReForm-Eval benchmark, offering substantial data for evaluating various capabilities of LVLMs. Through extensive experiments and analysis in ReForm-Eval, we demonstrate the comprehensiveness and reliability of ReForm-Eval in assessing various LVLMs. Our benchmark and evaluation framework is now available at https://***/FudanDISC/ReForm-Eval © 2024 ACM.

关键词： Visual languages

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Comparison of Standard Ant Colony Optimization, Dijkstra and A* Algorithms for Path Planning of Robot in an Indoor Environment

Comparison of Standard Ant Colony Optimization, Dijkstra and...

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Mechanical engineering and Robotics Research (ICMERR), International Conference on

作者： Sudeep Sharan Anh Tong Ngoc Minh Juan José Domínguez-Jiménez Peter Nauth Faculty of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany UCASE Software Engineering Group School of Engineering University of Cádiz Cadiz Spain

ISBN: (数字)9798331509262

ISBN: (纸本)9798331509279

The research topic of Path planning is extremely challenging area of concentration within the field of mobile robots. However, path planning algorithms for mobile robot tasks are contingent upon the environment and its level of complexity. This paper analyzes four distinct path planning methods for simulated indoor environment. The proposed algorithms include conventional Ant Colony methods, Ant System (AS), Ant Colony System (ACS), as well as standard methods Dijkstra and AStar (A*). We analyzed and examined these algorithms by employing various metrics/maps with complexity. The results indicate that the traditional path planning algorithm Dijkstra and A* approaches surpass the other ant colony techniques in terms of both computation time and path distance.

关键词： Ant colony optimization Heuristic algorithms Stars Path planning Real-time systems Indoor environment Complexity theory Mobile robots Standards Testing

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities 7

The Reliability of Information Sources: Student Perceptions ...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources;social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape. © 2024 IEEE.

关键词： Data reliability

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A robust deformed convolutional neural network(CNN)for image denoising

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 331-342页

作者： Qi Zhang Jingyu Xiao Chunwei Tian Jerry Chun‐Wei Lin Shichao Zhang School of Economics and Management Harbin Institute of Technology at WeihaiWeihaiChina School of Computer Science Central South UniversityChangshaChina School of Software Northwestern Polytechnical UniversityXi'anShaanxiChina Research&Development Institute Northwestern Polytechnical UniversityShenzhenChina Yangtze River Delta Research Institute Northwestern Polytechnical UniversityTaicangChina Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergenNorway

Due to strong learning ability,convolutional neural networks(CNNs)have been developed in image ***,convolutional operations may change original distributions of noise in corrupted images,which may increase training difficulty in image *** relations of surrounding pixels can effectively resolve this *** by that,we propose a robust deformed denoising CNN(RDDCNN)in this *** proposed RDDCNN contains three blocks:a deformable block(DB),an enhanced block(EB)and a residual block(RB).The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture,according to relations of surrounding *** EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers,batch normalisation(BN)and ReLU,which can enhance the learning ability of the proposed *** address long-term dependency problem,the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean ***,we implement a blind denoising *** results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative *** can be obtained at https://***/hellloxiaotian/RDDCNN.

关键词： blind denoising CNN deformed block enhanced block

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Classification Models and Variable Selection for SME Credit Risk Assessment in Balance the Data Set 10

Classification Models and Variable Selection for SME Credit ...

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作者： Intharasompong, Sornthun Jitpattanakul, Anuchit Mekruksavanich, Sakorn Wongwiwat, Piyada Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering 56000 Thailand Suansunandha Rajabhat University Faculty of Science and Technology Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans. © 2025 IEEE.

关键词： Risk assessment

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