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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Thiruvannamalai Sivasankar, P. Rengarajan, A. Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Department of Computer Science and IT Karnataka Bangalore India

ISBN: (纸本)9798350398267

The share of new electric electricity production will steadily rise with the development of new power grids, and the exchange interaction of simulated power stations would become increasingly difficult and complicated. The old centralized forecasting approach can not only match the demands of the growth of online energy plants due to the rise in the quantity of energy entities participating in them. The operation efficiency of imaginary power plants may be increased thanks to the decentralization, tamper tolerance, and cryptographic protocols of the bitcoin. The consensual latency of the bitcoin in concluding activities, therefore, prevents the Ethereum blockchain from growing as more scattered users participate in exchanges. This study first suggests a design for a ledger digital electricity generation network and explains the connections between and functions of every level. Eventually, Segment Byzantine Fault Resilience, a chain division unanimity mechanism for digital power plant payments, was put out (PPBFT). The method in this study may significantly minimize the acceptance time as the number of nodes rises and enhance the fairness and timeliness of payments, it is proven using C++ language to model the technique. © 2022 IEEE.

关键词： Blockchain

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Smart Diagnosis System for Diabetic Retinopathy

Smart Diagnosis System for Diabetic Retinopathy

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2023 International Conference on Artificial Intelligence science and Applications in Industry and Society, CAISAIS 2023

作者： Al-Haj, Yousif A. Al-Badawi, Badr A. Al-Nashad, Khadeja M. Al-Falah, Marwan M. Nasr, Akram Ali Othman Al-Dubaibi, Haitham A. Modern Specialized University Al-Saeeda University Faculty of Engg. & Smart Computing Faculty of Engg and Computer Science Artificial Intelligence Department Sana'a Yemen Faculty of Engg. & IT Sana'a Yemen Faculty of Computer Science and Information Technology Faculty of Engg. & IT Knowledge and Modern Science Sana'a Yemen

ISBN: (纸本)9798350314786

Diabetic retinopathy (DR) presents a substantial vision health challenge, demanding early detection. However, limited ophthalmologist resources and meticulous manual diagnosis hinder swift and precise identification. Leveraging cutting-edge advances in smart healthcare and AI, we introduce an innovative solution-A groundbreaking DR diagnostic system powered by EfficientNet CNN and transfer learning. Utilizing the APTOS 2019 Blindness Detection dataset ensures both efficiency and accuracy. Notably, our system stands out by offering a simultaneous diagnosis of various retinal diseases, setting it apart from previous efforts. Impressively, our model achieves a remarkable 93.80% accuracy, surpassing benchmarks across dimensions. Guided by user-friendly interfaces, doctors and assistants seamlessly input data, generate medical cards, and access patient reports. Balancing design, ImageNet pre-Training, and deep learning, our solution bridges ophthalmologist gaps, providing precise DR detection. Its potential extends further, promising improved patient care and broader medical applications. Amid AI-driven healthcare transformation, our pioneering intelligent diagnostic system shines as a beacon of positive impact. © 2023 IEEE.

关键词： Ophthalmology

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Predicting the Type of Narrow Bipolar Pulse: A Machine Learning Approach 12

Predicting the Type of Narrow Bipolar Pulse: A Machine Learn...

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12th International Conference on Mathematical Modeling in Physical sciences, IC-MSQUARE 2023

作者： Thabrew, K.A.S. Vayanganie, A. Fernando, M. Gunasekara, L. Abeywardhana, R. Sri Lanka Technological Campus Padukka Sri Lanka Department of Physics Faculty of Science University of Colombo Colombo03 Sri Lanka Faculty of Computer Science Dalhousie University HalifaxNS Canada

Narrow Bipolar Pulses (NBP) depicts the electric field (E-Field) changes due to a Compact Intra-cloud Discharge (CID) and are of two types namely Positive NBP (PNBP) and Negative NBP (NNBP). In this study, 437 NBPs were statistically investigated using a dataset collected in Sri Lanka in 2013, 2015, 2016 and 2017. Seven independent variables (Pulse Duration (PD), Rise Time (RT), Slow Front Duration (SFD), Zero Crossing Time (ZCT), Full Width at Half Maximum (FWHM) and Ratio between the Initial and Overshoot Peak Amplitudes (RIOPA)) and one dependent variable (Type of NBP) were analyzed. Two machine learning classification models, the Random Forest (RF) model and the Binary Logistic Regression (BLR) model, were used to predict the dependent variable based on the independent variables. Two models were compared in terms accuracy (ACC), sensitivity (SE), specificity (SP), AUC (Area Under the Curve)-ROC (Receiver Operator Characteristic) curve and kappa statistic. RF model scored the highest in terms of ACC (0.91), specificity (0.94), Kappa statistics (0.82) and AUC (0.98). In conclusion, RF model had the best performance in predicting the type of NBP hence can be used as a suitable automated method to classify the type of NBP. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Forecasting

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Properties of the Reed-Muller-Fourier Spectra of Maiorana-McFarland Bent Functions 53

Properties of the Reed-Muller-Fourier Spectra of Maiorana-Mc...

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53rd IEEE International Symposium on Multiple-Valued Logic, ISMVL 2023

作者： Moraga, Claudio Stanković, Radomir S. Stanković, Milena Tu Dortmund University Faculty of Computer Science Dortmund44221 Germany Faculty of Electronic Engineering Department of Computer Science Niš18000 Serbia Mathematical Institute Serbian Academy of Sciences and Arts Belgrade11000 Serbia

The Reed-Muller-Fourier spectrum of p-valued (p prime, p > 2) Maiorana-McFarland bent functions is studied. It is shown that this spectrum exhibits a unique structure, which allows characterizing these functions an... 详细信息

ISBN: (纸本)9781665464161

关键词： Fourier transforms

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The Sharma-Tasso-Olver-Burgers equation:its conservation laws and kink solitons

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Communications in Theoretical Physics 2022年第2期74卷 1-8页

作者： K Hosseini A Akbulut D Baleanu S Salahshour Department of Mathematics Near East University TRNCMersin 10Turkey Department of Mathematics-Computer Art-Science FacultyEskisehir Osmangazi UniversityEskisehirTurkey Department of Mathematics Faculty of Arts and SciencesCankaya UniversityAnkara 06530Turkey Institute of Space Sciences Magurele-BucharestRomania Faculty of Engineering and Natural Sciences Bahcesehir UniversityIstanbulTurkey

The present paper deals with the Sharma-Tasso-Olver-Burgers equation(STOBE)and its conservation laws and kink *** precisely,the formal Lagrangian,Lie symmetries,and adjoint equations of the STOBE are firstly constructed to retrieve its conservation *** solitons of the STOBE are then extracted through adopting a series of newly well-designed approaches such as Kudryashov and exponential *** graphs in 2 and 3D postures are formally portrayed to reveal the dynamical features of kink *** to the authors’knowledge,the outcomes of the current investigation are new and have been listed for the first time.

关键词： Sharma-Tasso-Olver-Burgers equation conservation laws Kudryashov and exponential methods kink solitons dynamical features

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Predicting Student Success in mathematics Using Machine Learning Techniques: A Comparative Study of Algorithms 1

Predicting Student Success in Mathematics Using Machine Lear...

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1st International Conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Samia, Zouaoui Houda, Zouaoui Nour El Hareth, Mestour University of Batna 2 Computer Science Department Batna Algeria Linfi Laboratory of Biskra University Algeria University of Biskra Laboratory of Applied Mathematics Mathematics Department Algeria University of Batna 2 Mathematics Department Batna Algeria

ISBN: (纸本)9798331541064

Achieving low results in mathematics is a real and urgent concern, as mathematics is the foundation of science and technology, and any weakness in it reflects negatively on the progress of society in various ways. This study evaluates how different mathematics study methods affect students educational success and achievement. We employed three different machine learning models to analyze data related to mathematics study techniques and student outcomes. These methods included: support vector machine (SVM), A K-nearest neighbors (KNN), and Linear Regression (LR). The results showed that KNN provided the best accuracy rate with 97.6% and an F1-score of 96.7%. These results highlight the significant contribution of forming study groups and trying to cooperate with each other, this factor is considered important and influential in the success of students in mathematics. © 2024 IEEE.

关键词： Contrastive Learning

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An Innovative Tool for Uploading/Scraping Large Image Datasets on Social Networks 2

An Innovative Tool for Uploading/Scraping Large Image Datase...

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2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2023

作者： Arceri, Nicolo Fabio Giudice, Oliver Battiato, Sebastiano University of Catania Department of Mathematics and Computer Science Viale Andrea Doria 6 Sicily Catania95123 Italy

ISBN: (纸本)9798350300802

N owadays, people can retrieve and share digital information in an increasingly easy and fast fashion through the well-known digital platforms, including sensitive data, inappropriate or illegal content, and, in general, information that might serve as probative evidence in court. Consequently, to assess forensics issues, we need to figure out how to trace back to the posting chain of a digital evidence (e.g., a picture, an audio) throughout the involved platforms-this is what Digital (also Forensics) Ballistics basically deals with. With the entry of Machine Learning as a tool of the trade in many research areas, the need for vast amounts of data has been dramatically increasing over the last few years. However, collecting or simply find the 'right' datasets that properly enables data-driven research studies can turn out to be not trivial in some cases, if not extremely challenging, especially when it comes with highly specialized tasks, such as creating datasets analyzed to detect the source media platform of a given digital media. In this paper we propose an automated approach by means of a digital tool that we created on purpose. The tool is capable of automatically uploading an entire image dataset to the desired digital platform and then downloading all the uploaded pictures, thus shortening the overall time required to output the final dataset to be analyzed. © 2023 IEEE.

关键词： automation criminal procedure defamation Digital ballistics digital forensics social media tool chain

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An integrated framework based deep learning for cancer classification using microarray datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2249-2260页

作者： Alrefai, Nashat Ibrahim, Othman Shehzad, Hafiz Muhammad Faisal Altigani, Abdelrahman Abu-ulbeh, Waheeb Alzaqebah, Malek Alsmadi, Mutasem K. Johor Johor Bahru81310 Malaysia Johor Skudai81310 Malaysia Department of Computer Science and IT University of Sargodha Sargodha40100 Pakistan Computer Science Department College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of Information Technology Al Istiqlal University Jericho4728 Palestine Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

Around the world, cancer is one of the leading reasons of mortality. The importance of earlier detection and prognosis of cancer types is highly significant for patients’ health. In recent research, deep neural networks were trained using gene expression microarray, to classify cancer. Biologists are able to monitor thousands of genes in one experiment using microarray technology. Microarray datasets are considered high-dimensional data, as they are cluttered with irrelevant, redundant, and noisy genes that contribute insignificantly to classification. The most informative genes contributing to cancer classification have been identified using computational intelligence algorithms. In this paper, we propose an integrated framework for cancer classification. This framework is divided into three tasks. Firstly, particle swarm optimization with ensemble learning (PSO-ensemble) reduces the microarray dataset's high dimensionality. Secondly, The Adaptive self-training method (ASTM) is used to solve low-size issues. Finally, a Convolutional Neural Network (CNN) was employed for classification. CNN has the ability to discover the complex non-linear relationships between features and select the most informative. Transfer learning was used sequentially with CNN to integrate the classification procedure because it can reduce the training time and computational complexity. Six microarray datasets are used, namely liver, breast, colon, prostate, central nervous system, and lung. The proposed CNN architecture with transfer learning provided 100% classification accuracy for colon, prostate, CNS and lung microarray datasets, and 97.62%, 95.45% accuracy for liver and breast cancer respectively. Experiments show that our proposed method delivers the highest classification accuracy and reduces training time with the smallest gene subset. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolutional neural networks

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Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation Systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM Machine learning

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction 14

An Attention-Based Model for Clinical Time Series Prediction...

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14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Madinei, Hananeh Sadat Keyvanpour, Mohammad Reza Shojaedini, Seyed Vahab Data Mining Laboratory Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Computer Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis. ©2024 IEEE.

关键词： Electronic health record

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