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Intelligent Signal Processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Data Science and Quantum Computing ABV Indian Institute of Information Technology and Management Gwalior India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

In non-stationary wireless fading channels deep learning models are effective in predicting channel states as indicated by this study. This work compared the LSTM and GRU models with autoregressive methods or Kalman filter, implying that deep learning is indeed able to capture temporal dependence between variables and was likely more adapted than linear method when dealing with many changes in dynamics. From the perspective of pre-processing data, keys like attention mechanism and transfer learning were decisive elements in helping them to improve prediction scores and make models more robust. These results showed that these enhanced procedures allow the models to generalise over a wide range of non-stationary situations, generating consistent performance in various diverse environments. This study indicates the promise of deep learning for enhancing wireless network operation, which could lead to greater self-awareness and utilization features in emerging future-generation wireless systems.

关键词： Deep learning Fading channels Adaptation models Attention mechanisms Wireless networks Transfer learning Predictive models data models Kalman filters Long short term memory

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application

Investigating Principle Component Analysis Impact on the Per...

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Information Technology (ACIT)

作者： Ahmed BaniMustafa Sattam Almatarneh Olla Bulkrock Raed Alazaidah Hossam Almahasneh Ghassan Samara Computer Information Science Dept. Higher Colleges of Technology Abu Dhabi UAE Data Science & Artificial Intelligence Dept. Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah KSA Software Engineering Dept. Princess Sumaya University for Technology Amman Jordan Computer Science Dept. Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion.

关键词： Training Logistic regression Cells (biology) Nearest neighbor methods Vectors Numerical models Classification algorithms Random forests Principal component analysis Biomedical imaging

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RSM-NLP at BLP-2023 Task 2: Bangla Sentiment Analysis using Weighted and Majority Voted Fine-Tuned Transformers

arXiv

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arXiv 2023年

作者： Seth, Pratinav Goel, Rashi Mathur, Komal Vemulapalli, Swetha Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Data Science & Computer Applications Dept. of Computer Science & Engineering Dept. of Information and Communication Technology India

This paper describes our approach to submissions made at Shared Task 2 at BLP Workshop - Sentiment Analysis of Bangla Social Media Posts(Hasan et al., 2023a;Islam et al., 2021;Hasan et al., 2023b). Sentiment Analysis is an action research area in the digital age. With the rapid and constant growth of online social media sites and services and the increasing amount of textual data, the application of automatic Sentiment Analysis is on the rise. However, most of the research in this domain is based on the English language. Despite being the world's sixth most widely spoken language, little work has been done in Bangla. This task aims to promote work on Bangla Sentiment Analysis while identifying the polarity of social media content by determining whether the sentiment expressed in the text is Positive, Negative, or Neutral. Our approach consists of experimenting and finetuning various multilingual and pre-trained BERT-based models on our downstream tasks and using a Majority Voting and Weighted ensemble model that outperforms individual baseline model scores. Our system scored 0.711 for the multiclass classification task and scored 10th place among the participants on the leaderboard for the shared task. Our code is available at https://***/ptnv-s/RSM-NLP-BLP-Task2. Copyright © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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Firefly Algorithm Approach for Recovering the Mathematical Structure of One-Dimensional Chaotic Maps from Time Series data

Firefly Algorithm Approach for Recovering the Mathematical S...

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Computer Technologies (ICCTech), International Conference on

作者： Akemi Gálvez Iztok Fister Andrés Iglesias Dept. of Applied Mathematics & Computational Sciences University of Cantabria Santander Spain Faculty of Electrical Engineering & Computer Science University of Maribor Maribor Slovenia

ISBN: (数字)9798350395419

ISBN: (纸本)9798350395426

Uncovering the mathematical structure of unknown chaotic systems from limited time series data poses a significant challenge in the field of dynamical systems, with broad applications across various domains. In this paper, we propose a swarm intelligence approach to tackle this issue, specifically focusing on one-dimensional discrete maps. Our approach leverages the firefly algorithm, a popular nature-inspired metaheuristic, known for its optimization capabilities. We assess the performance of the method by applying it to two illustrative examples of time series derived from one-dimensional maps: the Hénon map and the Burger map. The results show that this approach is promising for recovering the equations of motion of one-dimensional chaotic maps, even in situations where no additional information is available beyond the provided data points.

关键词： Heuristic algorithms Time series analysis Metaheuristics Focusing Particle swarm optimization Dynamical systems

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Automatic Identification of Teff (Eragrostis Tef) Flour Adulteration Using Machine Learning

Automatic Identification of Teff (Eragrostis Tef) Flour Adul...

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International Conference on Systems and Informatics (ICSAI)

作者： Zekiyos Abayneh Bochera Solomon Gizaw Onime Clement Dept. of Computational Data Science Addis Ababa University Addis Ababa Ethiopia Dept. of Computer Science Addis Ababa University Addis Ababa Ethiopia Information and Communication Technology Section (ICTS) Abdus Salam International Centre for Theoretical Physics (ICTP) Trieste Italy

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Food adulteration, the act of mixing foodstuffs with lower-quality foodstuffs or other food and non-food items, has become a global issue, particularly in developing countries like Ethiopia. This is a significant problem as it can lead to nutritional deficiencies and in some cases, illnesses, diseases or even death. The identification of adulterated food typically requires physio-chemical laboratory tests that vary depending on the type and level of adulterant. Teff flour, a daily staple food in Ethiopia, is often adulterated with wood flour and gypsum powder. In this research work, we utilized computer vision techniques to extract textural and color features from images of both adulterated and pure Teff flour. We then trained a Support Vector Machine (SVM) based machine learning model to identify adulterated Teff flour and predict the level of adulteration. The presented results show varying levels of prediction accuracy depending on the type of Teff flour, the level of adulteration. The developed model was also found to provide good results with images sizes ranging from $\boldsymbol{128 \times 128}$ to $\boldsymbol{2048 \times 2048}$ pixels. The results show overall model accuracy was above 90% when the adulteration level of red Teff flour is more than 25% and above 84% when the adulteration level of white wood flour was above 25%.

关键词： Support vector machines Computer vision Accuracy Powders Machine learning algorithms Image color analysis computational modeling Feature extraction Convolutional neural networks Testing

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UniFaaS: Programming across Distributed Cyberinfrastructure with Federated Function Serving

arXiv

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arXiv 2024年

作者： Li, Yifei Chard, Ryan Babuji, Yadu Chard, Kyle Foster, Ian Li, Zhuozhao Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Data Science and Learning Division Argonne National Laboratory LemontIL United States Dept. of Computer Science University of Chicago ChicagoIL United States

Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputers, clouds, and accelerators. Such applications call for new approaches to programming, distributed execution, and function-level management. We present UniFaaS, a parallel programming framework that relies on a federated function-as-a-service (FaaS) model to enable composition of distributed, scalable, and high-performance scientific workflows, and to support fine-grained function-level management. UniFaaS provides a unified programming interface to compose dynamic task graphs with transparent wide-area data management. UniFaaS exploits an observe-predict-decide approach to efficiently map workflow tasks to target heterogeneous and dynamic resources. We propose a dynamic heterogeneity-aware scheduling algorithm that employs a delay mechanism and a re-scheduling mechanism to accommodate dynamic resource capacity. Our experiments show that UniFaaS can efficiently execute workflows across computing resources with minimal scheduling overhead. We show that UniFaaS can improve the performance of a real-world drug screening workflow by as much as 22.99% when employing an additional 19.48% of resources and a montage workflow by 54.41% when employing an additional 47.83% of resources across multiple distributed clusters, in contrast to using a single cluster. Copyright © 2024, The Authors. All rights reserved.

关键词： Scheduling algorithms

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Integrated and Intelligent Elder-Friendly E-commerce Framework using NLP Techniques

Integrated and Intelligent Elder-Friendly E-commerce Framewo...

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IEEE Students' Conference on Electrical, Electronics and Computer science (SCEECS)

作者： Sivasanjith G Mohan Prabhu N Hemaswathi S Rajkumar P Rajeshkannan C R. Dhivya Dept. of Information Technology KARE Krishnankovil India Artificial Intelligence and Data Science Indra Ganesan College of Engineering Tiruchirappalli India Dept. of Computer Science and Engg. Mount Zion College of Engg. and Tech. Pudukottai India Dept. of Computer Science and Engg. Suguna College of Engineering Coimbatore India Dept. of Information Technology M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

All over the world, the population of elderly people is increasing a lot. There needs to be special attention to the welfare of the elderly person to make them confident in their independent quality of living. In recent years, researchers have shown much interest in smart home automation for people who live alone without any human assistance. However, the purchase of medical drugs, diapers, etc., outside the house has to be fulfilled by their family members or caretakers. In most cases, it is not feasible, as the family members may stay long away from the elders. Purchasing online is easy; however, analyzing the best product requires knowledge in each e-commerce application for better quality at a low cost. This paper proposes an integrated and intelligent e-commerce framework to avoid the drawback of purchasing items online in a single window using the NLP technique. The proposed framework provides offers, discounts, star ratings, and product reviews by analyzing other customers’ review data and purchase patterns. It provides interactive data analysis at a single window for the ease of elderly persons. The performance of the proposed framework was evaluated by 120 elderly persons using a simple questionnaire with a statistical analysis of a sample purchase.

关键词： Drugs Sentiment analysis Reviews Statistical analysis Urban areas Stars Smart homes Electronic commerce Older adults Recommender systems

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Enhanced Healthcare Through Metaverse Integration: Explainable AI and Blockchain for Trust, Security, and Immersive Experiences

Enhanced Healthcare Through Metaverse Integration: Explainab...

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computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Bilal Ibrahim Sowan Hazem Qattous Mohammad Fasha Ahmad Altarawneh Dept. of Bus. Intell. & Data Analytics University of Petra Amman Jordan Dept. of Software Engineering Princess Sumaya University for Technology Amman Jordan Dept. of Computer Science Brunel University of London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The metaverse, a dynamic blend of virtual and augmented reality, holds extraordinary promise for reshaping the healthcare landscape. By integrating advanced technologies such as virtual reality (VR), artificial intelligence (AI), blockchain, and digital twins, the metaverse introduces transformative opportunities in patient care, medical education, and healthcare operations. These tools foster immersive, personalized, and data-driven environments, significantly improving diagnostic precision, therapeutic strategies, and training methodologies. This paper provides a framework to utilize this potential while navigating key concerns, including systems scale, user customization, and ethical adequacy. In that case, the framework's goal should be eliminating existing limitations and barriers within healthcare systems by utilizing state-of-the-art technologies with real-world applications. The framework involves virtual consultation sites that enable provider-patient interactions, intelligent virtual agents with specific care recommendations, blockchain integration for data integrity, and dynamic VR segments that are functional in delivering healthcare education and training. The study focuses on scenario-based modeling and the use of pilot deployment in various healthcare settings to test for practicability and feasibility. The research also emphasizes that ethical issues and inclusion during the design and implementation of the innovations should also consider society's values regarding the medical field and promote equal access to healthcare. This framework proves a comprehensive blueprint for continuous progress and places the metaverse as one cornerstone in the global healthcare system.

关键词： Training Ethics Technological innovation Solid modeling Metaverse Medical services Blockchains Digital twins Security Medical diagnostic imaging

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Crowd Estimation Using Sensors for Public Transport (BUSES)

Crowd Estimation Using Sensors for Public Transport (BUSES)

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： S. Sumathi Nisha Soms S Gowtham M Pagalavan S Hariharan Dept. of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore KPR Institute of Engineering and Technology Coimbatore

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The Population in Indian metropolitan metropolises have been growing at an intimidating rate. In these metropolises, motorcars are generally the most preferred last afar trip, due to their cheap rates and expansive routes. Crowd estimation ways using cameras inside the motorcars aren’t suitable. Hence, we’ve enforced a result using Internet of effects. Crowd operation system is necessary in those circumstances to prevent crowd problems. Therefore, in order to preserve lives, it is very necessary for a frame to be in charge of these masses. In terms of crowd timing and used sweating, this frame shall be clever and efficient. The suggested framework is based on IoT, facilitates relationships between electronic devices, and presents information through smart operations with a tidy, straightforward interface for flexible and simple operation by individuals from all eras. The goal is to support directors’ control in an explosive way and distribute to callers over the designated area. The detector, operation, and affiliate layers make up the three layers that make up the frame. The detector subcaste is in charge of gathering crowd data. As a mediator between detectors and affiliate layers, the operation subcaste. It consists of online or other services that are in charge of gathering and analyzing data flowing from the detectors. Additionally, it alerts managers of busy places so they can act appropriately. Later, the appropriate choice will be made and communicated to the interface subcaste. The proposed crowded the machine is and how far is it from their position for which They’re staying. To reduce the vexation in the operation of Public Transport System(motorcars) through Integration of tackle and software technologies that provides stoner interface with 1. Live position of the machine (through GPS). 2. Crowd position in the machine (through Detectors).

关键词： Sociology Estimation Detectors Software Sensor systems Timing Sensors

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A Study on the Utilization of Green Hydrogen Energy - An Overview

A Study on the Utilization of Green Hydrogen Energy - An Ove...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ikumapayi, Omolayo M. Laseinde, Opeyeolu T. Ting, Tin T. Afolalu, Sunday A. Adetunla, Adedotun O. Ogedengbe, Temitayo S. University of Johannesburg Department of Mechanical and Industrial Engineering Technology South Africa INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Nilai71800 Malaysia Afe Babalola University Department of Mechanical and Mechatronics Engineering Ado-Ekiti Nigeria University of Johannesburg Dept. of Mechanical Engineering Science South Africa Afe Babalola University Department of Mechanical and Mechatronics Engineering Ado-Ekiti Nigeria Nile University of Nigeria FCT Department of Mechanical Engineering Abuja Nigeria

ISBN: (纸本)9798350358155

This review focuses on the use of hydrogen generated from renewable sources for energy generation and storage systems. Different strategies for producing, storing, and transporting environmentally friendly hydrogen will be covered in the first section of the examination. A critical analysis and choice of production method was studied. The study focused on the problems with transporting and storing hydrogen with a view to suggest solutions. The use of hydrogen in reducing carbon emissions was reviewed and an analysis of selected case studies was done with an emphasis on how hydrogen's carbon footprint varies depending on the gas source. © 2024 IEEE.

关键词： Carbon Energy Fuel Hydrogen

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