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Multi-Modal data Fusion Approach Using the Structured and Sparse Principal Component Analysis (MMSSCA)

Multi-Modal Data Fusion Approach Using the Structured and Sp...

Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Jyoti Kandpal A Jonitha Janaki Sivakumar Rejon Kumar Ray School of Computing Graphic Era Hill University Dehradun Uttarakhand India Faculty of Data Science & Computing Universiti Malaysia Kelantan Malaysia Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman Department of Business analytics Gannon University Erie PA USA

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

There are many different sorts of data that can be gathered and analyzed, including pictures, videos, texts, speeches, music, and other noises, Video content, for example, generally includes minimum some types of audible sounds or any type of visual image, and often also useful text data that can be of some specific type of titles or subtitles, which may consist of some unique type text language which is different type of speech. “Modality” is basically defines each different type of acquisition strategy. As natural phenomena tend to be quite complex, it is unusual for a single modality to provide exhaustive understanding of the phenomenon of interest. The process of combining and interpreting the useful data between the same modal or in different modal, and also interaction with the human beings with multi-modal data fusion &the accurate and deep interpretation and understanding of data obtained from the combining process from it, are central to the research challenge on multimodal data analysis. We contend that many of these challenges are shared by researchers in different fields. Both the “why” and the “how” of data fusion are addressed in this paper. Various examples from the realms of science and technology serve as inspiration for the first problem, which is then suggested by a various types of mathematical based model framework demonstrating the utility of data fusion. To solve the second problem, we introduce the idea of “diversity” as a central concept and discuss several data-driven solutions based on matrix and tensor decompositions, highlighting how they take into account diversity in the underlying data sets to address the aforementioned issues, this research suggests a novel approach for modeling multi data fusion implemented using sparse technique and structural analysis.

关键词： Visualization Tensors Diversity reception Noise data integration Music data models

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A Sensitivity Analysis of Cellular Automata and Heterogeneous Topology Networks: Partially-Local Cellular Automata and Homogeneous Homogeneous Random Boolean Networks

arXiv

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arXiv 2024年

作者： Glover, Tom Eivind Jahren, Ruben Martinuzzi, Francesco Lind, Pedro Gonçalves Nichele, Stefano Department of Computer Science OsloMet - Oslo Metropolitan University Oslo Norway Department of Computer Science and Communication Østfold University College Halden Norway Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo0164 Norway Center for Scalable Data Analytics and Artificial Intelligence Leipzig University Leipzig Germany Institute for Earth System Science & Remote Sensing Leipzig University Leipzig Germany Remote Sensing Centre for Earth System Research Leipzig University Leipzig Germany

Elementary Cellular Automata (ECA) are a well-studied computational universe that is, despite its simple configurations, capable of impressive computational variety. Harvesting this computation in a useful way has historically shown itself to be difficult, but if combined with reservoir computing (RC), this becomes much more feasible. Furthermore, RC and ECA enable energy-efficient AI, making the combination a promising concept for Edge AI. In this work, we contrast ECA to substrates of Partially-Local CA (PLCA) and Homogeneous Homogeneous Random Boolean Networks (HHRBN). They are, in comparison, the topological heterogeneous counterparts of ECA. This represents a step from ECA towards more biological-plausible substrates. We analyse these substrates by testing on an RC benchmark (5-bit memory), using Temporal Derrida plots to estimate the sensitivity and assess the defect collapse rate. We find that, counterintuitively, disordered topology does not necessarily mean disordered computation. There are countering computational"forces" of topology imperfections leading to a higher collapse rate (order) and yet, if accounted for, an increased sensitivity to the initial condition. These observations together suggest a shrinking critical range. © 2024, CC BY.

关键词： Sensitivity analysis

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TransformerFusionNet: A Real-Time Multimodal Framework for ICU Heart Failure Mortality Prediction Using Big data Streaming

TransformerFusionNet: A Real-Time Multimodal Framework for I...

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2024 International Conference on computer and Applications, ICCA 2024

作者： Saleh, Hager McCann, Michael El-Sappagh, Shaker Breslin, John G. Hurghada University Faculty of Computers and Artificial Intelligence Hurghada Egypt University of Galway Insight Research Ireland Centre for Data Analytics GalwayH91 TK33 Ireland Atlantic Technological University Letterkenny Ireland Atlantic Technological University Department of Computing Letterkenny Ireland Galala University Faculty of Computer Science and Engineering Suez Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt School of Engineering University of Galway GalwayH91 TK33 Ireland

ISBN: (纸本)9798350367560

This paper presents a real-time multimodal frame-work to enhance ICU mortality prediction for heart disease patients by integrating structured data, clinical notes, and big data streaming platforms. The proposed framework comprises two components: an offline multimodal model and a real-time pipeline. In the offline component, we proposed the Transformer-FusionNet model leverages BioBERT-Transformer for clinical notes and a Recurrent Neural Network (RNN) for structured data. The model integrates outputs through a concatenation layer to make predictions and is rigorously evaluated using the MIMIC-III dataset. It demonstrates superior performance across evaluation metrics and achieves an accuracy of 91.720%, precision of 91.85%, recall of 91.720%, and F1-score of 91.693%, outperforming other single and multimodal models. The real-time pipeline component incorporates Apache Spark and Apache Kafka for ingesting, preprocessing, and streaming structured data and clinical notes. This integration enables real-time mor-tality prediction with TransformerFusionNet, addressing critical gaps in the utilization of clinical notes in healthcare analytics. The framework significantly advances multimodal deep learning applications in healthcare by combining state-of-the-art models with scalable streaming technologies, offering a practical solution for improving clinical decision support systems and patient outcomes in real-time, especially in intensive care unit settings. © 2024 IEEE.

关键词： Recurrent neural networks

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Leveraging CNN-Transformer Hybrid Architecture for Accurate Breast Tumor Segmentation in Mammograms

Leveraging CNN-Transformer Hybrid Architecture for Accurate ...

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Women in data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Mudassar Ali Muhammad Zeeshan Tahir Iqra Mariam Khadija Nawaz Tariq Mahmood Samia Allaoua Chelloug College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science and Technology Zhejiang University Hangzhou China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China Faculty of Information Sciences University of Education Vehari Pakistan Artificial Intelligence and Data Analytics Lab CCIS PSU Riyadh SA Department of Information Technology CCIS PNU Riyadh SA

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Breast cancer detection through mammograms is very crucial for early diagnosis, and tumor segmentation is an important step in treatment planning. This paper proposes a novel approach by combining CNNs with transformers, which we call the Hybrid CNN + Transformer model, for the task of breast tumor segmentation. It leverages the strengths of CNN s in local feature extraction to capture fine details, such as tumor boundaries, and combines them with transformers that model long-range dependencies and contextual relationships across images for the exact delineation of tumor regions in complex tissue structures. Our methodology will be tested on the high-quality mammog-raphy dataset annotated with both benign and malignant lesions, the so-called INbreast dataset. Herein, the CNN backbone-first for instance, ResNet or EfficientNet-extracts the input mammo-gram hierarchically; features are fed into a transformer layer in order to capture global dependencies, after which the final segmentation mask is generated by using a U-Net style decoder for a pixel-wise tumor classification. The segmentation accuracies of our models demonstrated the significant improvement achieved in tumor segmentation, using Hybrid CNN + Transformer when compared with tra-ditional CNN-based models on challenging tumor boundaries and complex tissue patterns. An accurate, robust, completely automatic method for tumor segmentation will thus enable improved diagnostic feasibility on clinical grounds. Furthermore, the combination of learning at both local and global features improved generalizability to previously unseen mammogram images and therefore strengthened clinical applicability.

关键词： Image segmentation Adaptation models Accuracy Breast tumors Computational modeling Transformers Mammography Breast cancer Convolutional neural networks Tumors

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Weather of the dorm Wi-Fi ecosystem at the University of Colorado Boulder: Fall semester 2019 to spring semester 2020 - A case study of Wi-Fi including a campus response to the COVID-19 perturbation

arXiv

引用

arXiv 2021年

作者： McGrath, Jake Davis, Armen Curry, James Gartner, Orrie Rodrigues, Glenn Spielman, Seth Massey, Daniel Aeropace Engineering Applied Mathematics Office of Information Technology Office of Data Analytics Department of Computer Science

"Growing use of network-enabled technology in Institutions of Higher Education (IHEs) among students, staff, and faculty means that there has been increasing demand to adapt technology platforms and tools that transform student learning strategies, faculty teaching, research modalities, as well as general operations. In fact, many of the new modalities are a necessity for doing IHE business. In August 2019, our research team, at the University of Colorado Boulder, began collecting and analyzing data from the campus Wi-Fi network. A goal of the research was to answer the question of "what passive sensing" of the IHE's Wi-Fi might be able to tell you about the gross dynamics of the "Wi-Fi weather" in the IHE ecosystem? Or more generally, what does anonymized data tell us about the dynamics in a IHE's ecosystem. Anonymized data were made available by the University of Colorado Boulder (CU) Office of Information Technology (OIT). Our goal was to understand the campus' dynamical ecosystem as a reflection of its collected Wi-Fi data. Those data could then be used to develop forecast models and an understanding of the dynamics of the university ecosystem where the dynamics of Wi-Fi connected device count could be used as a proxy for the ebb and flow of large scale, and small scale, behavior in the ecosystem. The analogy with weather prediction seemed appropriate and a viable strategy. Starting Fall 2019, data were collected in the 'observational phase' (data collection is ongoing). In the 'analysis phase,' briefly touched on here, we applied Singular Spectrum Analysis (SSA) "eigen decomposition," to deconstruct Wi-Fi data from dorms, the central campus dining cafeteria, the recreation center, and other buildings on campus. That analysis led to the identification of clusters of buildings that behaved similarly. Several campus buildings are dual use and they are placed in different clusters. Just as in the case of models of the weather, a final component of this research was

关键词： Weather forecasting

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An Empirical Approach of Developing Domain Based Ontology in the Field of Bioinformatics

An Empirical Approach of Developing Domain Based Ontology in...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Devanolla Suresh E.Afreen Banu R. Karthikeyan Pramod Kumar Naik Safaa Hallol Mohammed Tareq Hafdhi Abdtawfeeq Department of Computer Science & Technology Madanapalle Institute of Technology & Science Madanapalle CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of CSE Artificial Intelligence and Data Analytics Sri Ramachandra Institute of Higher Education and Research Department of CSE Dayananda Sagar University Bengaluru Collage of Pharmacy National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

From a macro viewpoint, it is essential to comprehend the similarities across human illnesses in order to identify underlying mechanisms at the microscopic level. Previous studies have shown that employing machine learning methods or merging many data sources can improve performance in sickness similarity measurement. However, there is currently a gap in the development of a useful framework for information extraction and integration from a range of biological data using deep learning models. We introduce CoGO, a Contrastive learning system that predicts commonalities in illnesses based on gene networks and ontology structure. Gene ontology (GO) and gene interaction network domain information is integrated into CoGO using graph deep learning models. Graph deep learning models are first used to encode gene and GO word features from independent graph structure data. Gene and GO characteristics are then mapped via a nonlinear projection to a shared embedding space. Next, by using cross-view contrastive loss to increase the agreement of related gene-GO links, meaningful gene representations are generated. Finally, to calculate disease similarity, CoGO computes the cosine similarity of sickness representation vectors derived from related gene embeddings.

关键词： Deep learning Biological system modeling Soft sensors Microscopy Merging Ontologies Information retrieval

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Towards Smarter Supply Chains: Machine Learning Methodologies for Delivery Risk Assessment

Towards Smarter Supply Chains: Machine Learning Methodologie...

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

作者： Mohammad A Al Khaldy Feras Alnaimat Ahmad Al-Qerem Ali Mohd Ali Mohammed Rajab Business Intelligence Data Analytics University of Petra Amman Jordan Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Computer Science Department Zarqa University Jordan College of Engineering Technology Al-Balqa Applied University Jordan University Headquarter University Of Anbar Ramadi Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Supply chain management (SCM) has evolved into a core strategic component for enterprises seeking to gain a competitive edge in an increasingly interconnected global marketplace. With the ever-growing complexity and volume of data available-from procurement and production to distribution and last-mile delivery-organizations are turning to machine learning (ML) and big data analytics to optimize performance and stream-line operations. In this paper, we explore how integrating machine learning into various stages of the supply chain can unlock efficiencies, reduce delivery risks, and enable more informed, data-driven decisions. We employ the dataCo Global Supply Chain dataset as a case study to illustrate how ML classifiers, including K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, and Random Forest, can be leveraged to predict late deliveries. Our results show that Random Forest achieved the highest accuracy (over 93%), demonstrating its superior ability to identify at-risk shipments. Furthermore, we emphasize the role of data exploration, feature engineering, and the critical need for addressing data imbalance in ensuring robust model performance. The discussion underscores the transformative potential of ML for improving supply chain efficiency, resilience, and strategic decision-making, while also highlighting future directions to handle evolving customer demands and emerging challenges in an era of rapidly expanding data.

关键词： Technological innovation Accuracy Costs Supply chains Customer satisfaction Nearest neighbor methods Big data Turning data models Random forests

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Comparative Analysis of Robotic Arm Efficiency: Evaluating PID and Fuzzy-PID Control using Printed Mechanical Structure and ESP8266 Integration

Comparative Analysis of Robotic Arm Efficiency: Evaluating P...

引用

International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Padma Nyoman Crisnapati I Made Darma Susila I Komang Agus Ady Aryanto Jaturapith Krohkaew Ricky Aurelius Nurtanto Diaz I Made Suandana Astika Pande Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Dept. of Big Data Management and Analytics Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Thanyaburi Thailand

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This study investigates the comparative effectiveness of manual PID tuning versus Fuzzy-PID control in enhancing the performance of a robot arm with two degrees of freedom (2-DOF). The experimental approach involves conducting two scenarios: manual tuning of PID parameters in Scenario 1 and utilizing a fuzzy algorithm for PID parameter tuning in Scenario 2. The novelty of this research lies in the new 3D mechanical design of the 2-DOF robot arm and the utilization of the ESP8266 microcontroller for implementing Fuzzy-PID control. Performance evaluation is conducted using Root Mean Square Error (RMSE) and Root Mean Square Percentage Error (RMSPE) calculations. The results reveal that Fuzzy-PID control significantly reduces errors compared to manual PID tuning, with lower RMSE and RMSPE values indicating more precise and stable control. These findings underscore the potential of integrating fuzzy logic with PID control to enhance system adaptability and flexibility. Additionally, the ESP8266 is capable of handling the computations required for fuzzy-PID control while being cost-effective.

关键词： Fuzzy logic PI control Microcontrollers 2-DOF Manuals Manipulators Three-dimensional printing PD control Root mean square Tuning

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Predicting Chronic Kidney Disease Using Machine Learning Algorithms

Predicting Chronic Kidney Disease Using Machine Learning Alg...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Afia Farjana Fatema Tabassum Liza Parth Pratim Pandit Madhab Chandra Das Mahadi Hasan Fariha Tabassum Md. Helal Hossen Department of Computer Science University of South Dakota Vermillion South Dakota USA Computer Science Florida State University Tallahassee Florida USA Mechanical and Manufacturing Engineering Miami University Oxford Ohio USA Electrical and Electronic Engineering University of information and technology Dhaka Bangladesh Data Analytics The University of Tennessee at Chattanooga Chattanooga Tennessee USA Department of Sociology Western Michigan University Kalamazoo Michigan USA Department of Computer Science The University of Texas at Dallas TX USA

In the modern era, everyone tries to be aware of their health, but because of their workload and hectic schedules, they only pay attention to it when certain symptoms appear. However, because CKD (Chronic Kidney disease) is a disease with no symptoms or, in some cases, no symptoms at all, it is difficult to predict, detect, and prevent such a disease, which could result in long-term health damage. However, machine learning (ML) offers hope in this situation because it excels at prediction and analysis. In this paper, we proposed nine ML approaches, such as K-nearest neighbors (KNN), support vector machines (SVM), logistic regression (LR), Naïve Bayes, Extra tree classifiers, AdaBoost, Xgboost, and LightGBM. These predictive models are built using a dataset on chronic kidney disease with 14 attributes and 400 records to choose the best classifier for predicting chronic kidney illness. The dataset was gathered via ***. Additionally, this study has compared how well these model's function. With the LightGBM model, we could predict kidney illness more accurately than ever before, with a 99.00% accuracy level.

关键词： Support vector machines Schedules Machine learning algorithms Sociology Static VAr compensators Predictive models Chronic kidney disease

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Industrial Internet of Things (IIoT): Transforming Industrial Operations through Advanced Connectivity

Industrial Internet of Things (IIoT): Transforming Industria...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： S. Viveka Vivek Justus Sangeetha J Desidi Narsimha Reddy V. Samuthira Pandi S. Banumathi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of CSE S. A. Engineering College Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The Industrial Internet of Things (IIoT) seamlessly integrates operational technology with modern connectivity technologies, ushering in a new age in industrial operations. This paper examines IIoT's revolutionary potential in changing industrial landscapes through smart sensors, actuators, and intelligent devices that enable real-time data sharing and process automation. This paper uses case studies from numerous sectors to demonstrate how IIoT may improve operational efficiency, downtime, and decision-making. Manufacturing, energy, and logistics are discussed. The report also examines IIoT technologies like cloud, edge, and machine learning techniques. These help enterprises exploit enormous data sets for predictive maintenance, supply chain optimization, and product quality. The article discusses IIoT's risks and challenges, including data privacy, cybersecurity, and the need for skilled personnel. A detailed research shows IIoT's strategic importance in the fourth industrial revolution. It also discusses how industrial networking will enable smart and sustainable operations.

关键词： Actuators Supply chains Real-time systems Product design Telecommunication computing Quality assessment Personnel Intelligent sensors Industrial Internet of Things Predictive maintenance

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