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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Abbas, Jamal K. Al-Azzawi, Waleed Hassan, Sharmeen Izzat Sharif, Salah Farhan A Manee, Mariam J. Al-Nisour University College Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Al Kitab University Department of Medical Engineering Kirkuk Iraq Uruk University Department of Radiology Techniques Baghdad Iraq

ISBN: (纸本)9798331534950

Financial time series forecasting is one of the most challenging problems in business and economics. Time series prediction has long been a prominent field of research because of the vast range of information that can be captured in time series. Deep learning approaches, such as automatically managing temporal variables like pattern and seasonality and automatically learning temporal connections, enable forecasts in time series. Analyzing financial time series to forecast future advancements has been challenging for many years. This study suggests the Deep learning-assisted Improved Financial Time Series Forecasting Model (DL-IFTSFM) to forecast future events and trends for client financial investment. The final performance of any system trained to function by analyzing historical data is greatly impacted by the division of data into training, validation, and test sets. This notion is supported by data on the temporal structure of the financial industry, which shows that the fundamental relationship between predictions and reactions varies with time. This paper describes various practical aspects of deep learning in economic time series forecasting, including choosing the best models and setting hyperparameter values. The suggested DL-IFTSFM model outperforms the current models in terms of prediction accuracy (97.2%), recall (97.9%), F-measure (96.2%), performance (98.9%), and error rate (10.2%), according to numerical results. © 2024 IEEE.

关键词： Prediction models

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New physical structures and patterns to the optical solutions of the nonlinear Schrödinger equation with a higher dimension

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Communications in Theoretical Physics 2023年第8期75卷 25-41页

作者： Karmina K Ali Abdullahi Yusuf Marwan Alquran Sibel Tarla Department of Mathematics Faculty of ScienceUniversity of ZakhoZakhoIraq Department of Computer Science College of ScienceKnowledge UniversityErbil 44001Iraq Department of Computer Engineering Biruni UniversityIstanbulTurkey Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Mathematics Near East University TRNCMersin 10Nicosia 99138Turkey Department of Mathematics and Statistics Jordan University of Science&TechnologyIrbid 22110Jordan Department of Mathematics Faculty of ScienceFirat UniversityElazigTurkey

It is commonly recognized that,despite current analytical approaches,many physical aspects of nonlinear models remain *** is critical to build more efficient integration methods to design and construct numerous other unknown solutions and physical attributes for the nonlinear models,as well as for the benefit of the largest audience *** achieve this goal,we propose a new extended unified auxiliary equation technique,a brand-new analytical method for solving nonlinear partial differential *** proposed method is applied to the nonlinear Schrödinger equation with a higher dimension in the anomalous *** interesting solutions have been ***,to shed more light on the features of the obtained solutions,the figures for some obtained solutions are *** propagation characteristics of the generated solutions are *** results show that the proper physical quantities and nonlinear wave qualities are connected to the parameter *** is worth noting that the new method is very effective and efficient,and it may be applied in the realisation of novel solutions.

关键词： exact solutions nonlinear Schrodinger equation new extended unified auxiliary equation method Jacobi elliptic functions

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Reviewing Facial Insight - A Smart Approach to Identity Unveiling Through Active Facial Patches and Multi-Task Cascaded Convolutional Networks 5

Reviewing Facial Insight - A Smart Approach to Identity Unve...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kavin, F. Pradeepa, K. Selvalakshmi, B. Anitha, P. Subashini, K. Vijayalakshmi, P. Cyient Maharashtra Navi Mumbai India St. Michael College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Sivaganga India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sai Renganathan Engineering College Department of ECE Tamil Nadu Coimbatore India Tagore Engineering College Dept. of Information Technology Tamil Nadu Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem Tamil Nadu Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

This research work proposes an innovative method for enhancing facial recognition accuracy and efficiency by leveraging active facial patches and a Multi-Task Cascaded Convolutional Network (MTCNN). The review assesses the paper's contributions, methodology, experimental results, and potential impact on advancing facial recognition technology. It highlights the strengths of the proposed approach, including its clear problem statement, thorough methodology, compelling experimental results, and potential for real-world application. Additionally, the review offers recommendations for improvement, such as further exploration of potential biases and scalability considerations. Overall, Facial Insight presents a promising advancement in identity unveiling within facial recognition systems, contributing to the broader field of biometrics and computer vision. © 2024 IEEE.

关键词： Biometrics

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Strategic Management of Advertising Using National Broadband IoT and Machine Learning 2

Strategic Management of Advertising Using National Broadband...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Abdulhasan, Maki Mahdi Safi, Hassan Ali, Abdalsalam Taha Hussain Othman, Nashwan Adnan Mahmoud, Mina Ismail Al-Nisour University College Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Al Kitab University Department of Medical Engineering Kirkuk Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq University: Uruk University Department of Radiology Techniques Baghdad Iraq

ISBN: (纸本)9798331534950

The development of Integrated Marketing Communications (IMC) in reaction to the fierce competition in modern marketplaces is a prime illustration of how distance marketing has evolved. IMC emerged as a marketing powerhouse around the turn of the century to provide a unified brand message across all promotional channels. This study suggests a new strategic architecture called IMC-NB-IoTML that combines IMC with NB-IoT and AI to improve ad management. This study evaluates existing corporate applications that combine IoT and machine learning and identifies obstacles to IMC's progress. Companies may use this architecture to their advantage by evaluating data collected from IoT devices and incorporating it into their sales and marketing campaigns to better understand customer behaviour. Machine learning takes this strategy to the next level by facilitating more accurate categorization of consumer data and enhancing the relevance and targeting of advertising campaigns. The simulation findings show that NB-IoT is the best strategy for making precise data-driven marketing decisions because it surpasses other recall, precision, and F-measure approaches. In today's linked data-rich environment, this article examines several NB-IoT and machine learning technologies inside an IMC framework, demonstrating how they can improve and enhance strategic advertising management. © 2024 IEEE.

关键词： Adversarial machine learning

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Next-Gen Education with 6G Connectivity and Intelligent Data Analytics 2

Next-Gen Education with 6G Connectivity and Intelligent Data...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Abbas, Jamal K. Hassan, Sharmeen Izzat Mohsen, Abdul Hussein Hussain, Ahmed Hussain, Faris Hassan Taha Al-Nisour University College Baghdad Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Uruk University Department of Medical Laboratory Techniques Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Al Kitab University Department of Medical Engineering Kirkuk Iraq

ISBN: (纸本)9798331534950

The aims to assess the efficacy of data extraction in educational software and determine the function of 6G IoT connectivity in improving educational systems. An Intelligent Data Analytics Model (IDAM) is suggested in this article to revolutionise evaluations of educational quality. Initial steps in IDAM's theoretical groundwork in education data mining and the Internet of Things (IoT) include collecting, organizing, and analyzing data from higher education and graduate outcomes. One goal is to use survey data and quantitative analysis to answer the problems that arise when comparing the quality of education at different schools. By utilizing insights from data-mining techniques utilized at the University of Science and Technology, the model intends to create an improved framework for educational quality assessment. The efficacy of this model will be evaluated in subsequent analyses. By linking IoT sensors and interacting with protocols to record learning behaviours, the results show that 6G IoT gear can improve educational monitoring. Passing information from the layer of perception to the protocol stack enables this system to provide real-time insights regarding instructional assets and educational quality. Overall, educational outcomes are improved in a connected, data-driven environment when the Internet of Things (IoT) is integrated with data-gathering tools. This allows for continuous assessment of educator performance. © 2024 IEEE.

关键词： Data collection

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Evaluating College Students' Physical Health Using the AHP Algorithm an Analytical Approach 2

Evaluating College Students' Physical Health Using the AHP A...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Mohammed, Abed Jawad Al-Taee, Mustafa M. Mohsen, Abdul Hussein Burhan Rajab, Adnan Kasim Khalil, Salim Yahya Al-Nisour University College Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Uruk University Department of Medical Laboratory Techniques Baghdad Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Al Kitab University Medical Engineering Kirkuk Iraq

ISBN: (纸本)9798331534950

Physical Exam (PE) tests demand a lot from teachers and pupils. A structured, objective clinical evaluation assesses interns' and students' vascular physical examination skills. These subjects are taught fundamentally. Instructor checking is challenging owing to teacher and student software variances. Inconsistencies make analyzing student qualifications and nearly no remarks impossible. This study shows how recording running commentary simultaneously can improve a movie to give students meaningful feedback that helps them learn and assess their competency. Data collection and interpretation by medical experts and students are erroneous. Those evaluated seem to have benefited from the internship. Interns assessed a year later showed some progress. To help college students achieve and grow analytical hierarchy competencies, the CSPE-AHP physical test is supplied. This technology could examine pupils' morphological changes, informing talent development best practices. This study shows surgical residents need better physical examination teaching. The student achievement ratio is 85.7%, the academic sports ratio is 86.5%, the involvement ratio is 83.4%, the e-learning activity ratio is 82.6%, the physical fitness test ratio is 91.2%, and the physical examination ratio is 90.25%. © 2024 IEEE.

关键词： Students

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Internet of Things-Based Analysis of Smart Grid Management 2

Internet of Things-Based Analysis of Smart Grid Management

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Jasim, Jasim Ahmede Yahya Kasim Khalil, Salim Hameed, Nema H. Safi, Hassan Mohammed, Marwan Aziz Al-Nisour University College Baghdad Iraq Medical Engineering Al Kitab University Kirkuk Iraq Uruk University Department of Anesthesia Techniques Baghdad Iraq Engineering Techniques Al-Farahidi University Department of Medical Instruments Baghdad10011 Iraq College of Engineering Knowledge University Department of Computer Engineering Erbil Iraq

ISBN: (纸本)9798331534950

Smart grid technology relies heavily on network technologies, which enable bidirectional communication between power plants, distribution hubs, and control rooms. Smart meters use cutting-edge measurement equipment widely installed and linked to the network in a smart grid. Because of its coherent architecture, the power grid is critical to repair and operate. It has left electric grid networks vulnerable to cyberattacks, recently becoming a concern for energy specialists. This article proposes an Internet of Things-based Smart Grid Management (IoT-SGM) system for managing smart grids and monitoring consumer load requirements. Constructing a networked smart meter is the first step in understanding fraudsters' motivations and identifying appropriate risk mitigation strategies. The current contribution describes developing, producing, and operating an Internet of Things smart meter built on the Arduino platform and integrated into a dependable electricity supply. The trial findings demonstrate a high detection accuracy ratio of 80.3%, a security level of 78.1%, a precision ratio of 89.5%, and a latency of 91ms when compared to other approaches. © 2024 IEEE.

关键词： Smart power grids

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Accuracy Detection in Automated Smart Grids Using a Stochastic Machine Learning Algorithm 2

Accuracy Detection in Automated Smart Grids Using a Stochast...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Qasim, Mohammed Nada Jaafar, Mohammed M. Rajab, Adnan Burhan Alani, Hithaifa Sharif, Shukur Ramadan Hama Al-Nisour University College Baghdad Iraq Uruk University Department of Optical Techniques Baghdad Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Al Kitab University Medical Engineering Kirkuk Iraq

ISBN: (纸本)9798331534950

This paper provides a computer vision aggregation integral equation approach for human-level perception of automated intelligent grids by strategically arranging micro-synchrophasor components while keeping distribution system reconfigurability in mind. The suggested stochastic technique enables phase-accurate placement of micro-synchrophasor components. Network segmentation and tying switching must consider typical traffic and demands. When utilizing this strategy, the designer's zero-injection limits can be leveraged to narrow the problem space even further. Furthermore, a novel strategy based on the stochastic whale optimizer method (SWOM) is proposed to improve assessment metrics and find the ideal architecture for each step, thereby reducing power consumption and customer interruption costs. The micro-synchrophasor localization problem is addressed using integral equations, while the restructuring technique employs the SWOM heuristic. One method for coping with the unpredictability of device placement or predicting errors is a stochastic methodology based on point estimation. Simulations and quantitative analysis of an actual system show that the proposed technique provides supply chain visibility from configuration commencement to completion within the planned time frame. Maintaining dependability under different loads, network configurations, and topologies is demonstrated. © 2024 IEEE.

关键词： Integral equations

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Real-Time Myanmar Hand Gestures Recognition Using Deep Learning Models 8th

Real-Time Myanmar Hand Gestures Recognition Using Deep Learn...

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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Kyaw, Nwe Ni Mitra, Pabitra Sinha, G.R. Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal721302 India Indian Knowledge System Lab International Institute of Information Technology Bangalore Karnataka Bengaluru560100 India

ISBN: (纸本)9789819738588

The proposed system for recognizing Myanmar sign language between individuals who are deaf. The aim of this study is to develop deep learning models for the purpose of accurately identifying dynamic hand gesture images depicting 14 classes of Myanmar words and numbers. The present system utilizes a new dataset comprising 7834 images for 14 classes obtained through Webcam. The objective of this study is to enhance the robustness of the new model. Next, we proceed to evaluate the proposed model by comparing them to those of existing convolutional neural network models. These models are generally recognized as effective deep-learning approaches for recognition tasks. The proposed model exhibits a performance level of 96% for both validation and testing accuracies. Furthermore, the experimental finding demonstrates that the model, convolutional layers, achieved an accuracy of 100% in recognizing Myanmar Sign Language (MSL). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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