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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Raval, Jay Verma, Jai Prakash Islam, Sardar N.M. Jain, Rachna Thakur, Narina Institute of Technology Nirma University Computer Science Engineering Department Ahmedabad India Victoria University Applied Informatics Research Melbourne Australia Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665453110

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm. © 2022 IEEE.

关键词： Data mining

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Career-Based Explainable Course Recommendation 18th

Career-Based Explainable Course Recommendation

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18th International Conference on Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, iConference 2023

作者： Striebel, Jacob Myers, Rebecca Liu, Xiaozhong Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States

ISBN: (纸本)9783031280313

The type and quality of education that a student receives can have a profound impact on their career. In contrast to education that is not intentionally organized to help students achieve specific career objectives, career-based education seeks to provide students with the skills that are required to achieve specific career goals. In this work, we propose a course recommendation framework that is designed to recommend courses based directly on their ability to teach skills relevant to a user-specified career goal. Within our framework, course recommendations are generated in a transparent manner, using skills to bridge between jobs and courses in a knowledge-based inference procedure. Due to the procedure’s transparency, our system is able to provide faithful template-based explanations detailing why each recommended course was chosen for recommendation. Our framework contrasts with other course recommendation systems in the literature that lack the ability to explain their choices and therefore may lack trustworthiness from the perspective of a user. The proposed framework has several applications, including assisting students with course planning, as well as aiding with curriculum evaluation and development by providing insight into the usefulness of specific courses to specific careers. We conduct a preliminary evaluation of our system, and its performance is competitive against two baselines. We provide all the resources needed to reproduce our results. (http://***/striebel/cbecr ) © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1877-1890页

作者： Mamta Brij B.Gupta Kuan-Ching Li Victor C.M.Leun Kostas E.Psannis Shingo Yamaguchi IEEE the Department of Computer Engineering National Institute of Technology the Asia University the Macquarie University the Department of Computer Science and Information Engineering Providence University the Computer Science and Software Engineering Shenzhen University the University of British Columbia the Department of Applied Informatics University of Macedonia the Graduate School of Sciences and Technology for Innovation Yamaguchi University

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret

关键词： Cloud-based cyber-physical systems(CCPS) data encryption healthcare information search and retrieval keyword search public-key cryptosystems searchable encryption

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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Optimized Price Prediction of Cryptocurrencies using Deep Learning on High-Volume Time Series Data

Optimized Price Prediction of Cryptocurrencies using Deep Le...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Gerasimos Vonitsanos Andreas Kanavos Foteini Grivokostopoulou Spyros Sioutas Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Computer Technology Institute and Press “Diophantus“ Patras Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Statistical models, enhanced by deep learning techniques, have become pivotal in various predictive tasks, including financial forecasting. This paper addresses the challenge of predicting cryptocurrency prices, utilizing a dataset comprising various cryptocurrencies treated as time series. We employ several deep neural network architectures-including Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Bidirectional LSTM networks-to forecast future prices. Our methodology involves a detailed analysis of cryptocurrency time series to inform the design of these networks. The performance of each model is rigorously compared, highlighting their predictive capabilities in the context of cryptocurrency markets. This study not only contributes to the empirical literature by applying advanced neural networks to high-volume financial data but also provides a comparative analysis that may guide future applications of deep learning in economic forecasting.

关键词： Deep learning Biological system modeling Time series analysis Predictive models Multilayer perceptrons Economic forecasting Big Data applications Cryptocurrency Context modeling

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A Robust Preprocessing Method for Measuring Image Visual Quality Using Log-Polar FFT Features 8th

A Robust Preprocessing Method for Measuring Image Visual Qua...

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8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

作者： Chen, Guang Yi Krzyzak, Adam Valev, Ventzeslav Department of Computer Science and Software Engineering Concordia University MontrealQCH3G 1M8 Canada Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia1113 Bulgaria

ISBN: (纸本)9783031495519

To register two images, existing methods normally estimate the parameters of an affine transform, and then perform an inverse affine transform to the test image by using the estimated parameters. Any metric can then be applied to the reference image and the normalized image. In this paper we propose a new method for measuring visual image quality which circumvents the need to estimate the parameters of the affine transform. Instead, we use the log-polar transform and the fast Fourier transform (FFT) to extract features that are invariant to translation, rotation, and scaling. We apply the existing structural similarity index measure (SSIM) to the two invariant feature images, where no inverse transform is needed. Experimental results show that our proposed method outperforms the standard metric, the mean SSIM (MSSIM), significantly in terms of visual quality scores. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image visual quality quality metrics rotation invariance scale invariance translation invariance

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Games for Scrum Team Collaboration in the Global Software Development Environment: A Literature Review 7

Games for Scrum Team Collaboration in the Global Software De...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Hidayati, Anita Ismail, Iklima Ermis Yuly, Ade Rahma Edison, Henry Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia Blekinge Institute of Technology Department of Software Engineering Sweden

ISBN: (纸本)9798350345711

Software development companies commonly use Global Software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies. © 2022 IEEE.

关键词： Software design

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Indoors Traversability Estimation with RGB-Laser Fusion 19

Indoors Traversability Estimation with RGB-Laser Fusion

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Sevastopoulos, Christos Theofanidis, Michail Hebri, Aref Konstantopoulos, Stasinos Karkaletsis, Vangelis Makedon, Fillia University of Texas at Arlington Department of Computer Science & Computer Engineering ArlingtonTX United States University of Texas Arlington Research Institute Fort WorthTX United States Institute of Informatics and Telecommunications Ncsr 'Demokritos' Agia Paraskevi Attica Greece

ISBN: (纸本)9798350320695

We propose a dual-stream, semi-supervised, attention-based approach that employs feature fusion of RGB and Laser Range Finder (LRF) modalities. Our method lever-ages the strength of two powerful transformer-based networks, i.e. Vision Transformer (ViT) and SegFormer, along with LRF information, to adequately predict whether the scene encountered in the image is safe for a robot to traverse. Towards this effort, we introduce an automated labelling system profiting from the combination of raw velocity readings and laser scanning information. Moreover, we show that overall GOINO-GO detection is enhanced by fusing RGB and laser modalities. Feature fusion is achieved through the employment of a Multi-Head Self-Attention (MHSA) module. Through cross-domain validation, we show that the proposed traversability estimation method can achieve decent amounts of transferability even with limited amount of training data. © 2023 IEEE.

关键词： Range finders

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Text Analysis and Recognition of Emotional Content Using Deep Learning Methods and BERT

Text Analysis and Recognition of Emotional Content Using Dee...

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International Conference on computer and Information Science (ACIS)

作者： Efthymios Andrikakis Isidoros Perikos Michael Paraskevas Ioannis Hatzilygeroudis Department of Computer Engineering & Informatics University of Patras Patras Greece Department of Computer Engineering & Informatics University of Patras and Computer Technology Institute and Press “Diophantus” Patras Greece

Recognizing the emotional content of Natural Language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. In this paper, deep learning, deep neural networks as well as Transformers were examined. Specifically, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. The results of the study show that BERT achieved the best overall performance, getting an F1-score of 0.86 on the Twitter Data dataset. Also, the Bi-LSTM with Attention Mechanism and Bi-LSTM using Word2vec performed quite well, achieving F1-scores of 0.83 and 0.82, respectively.

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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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