Fusion System for Blockchain Asset Securitization Risk Control Using Adaptive Deep Learning-Based Framework

Raed; Omar Saad; Talib A.; Wasfi; Riyam K.

doi:https://doi.org/10.54216/FPA.110206

Fusion: Practice and Applications | 2023

Multilevel Features Fusion of Intelligent Techniques for Brain Imaging Analysis

With the use of multi-level features fusion, this work provides a new method for recognizing cognitive brain activity, which we term the Improved Multi-modal cognitive brain-imaging method (IMCBI). Identifying brain areas and basing judgments on insights into intelligent cognitive behavior for babies and adolescents presents a number of methodological issues that the suggested approach seeks to address. In order to understand how the brain functions during various motor, perceptual, and cognitive tasks, IMCBI employs smart methods for fusing data at several levels. This technique employs functional magnetic resonance imaging (fMRI) data to assess human behavioral activity in the brain while engaging in a variety of activities. It does so by combining an inter-subject retrieval strategy with deep neural networks (DNN). The research shows that the suggested method, which uses multi-level fusion of features, greatly raises the accuracy ratio to 95.63 percent, the sensitivity to 95.42 percent, and the specificity to 94.3 three point three percent. The findings demonstrate the method's efficacy in recognizing brain activity based on high-level cognitive ability, making it a useful tool for predicting clinical and behavioral responses.

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Talib A. Al-Sharify mail -

Mohammed Hussein Ali mail -

Aqeel Hussen mail -

Zaid Saad Madhi mail

link https://doi.org/10.54216/FPA.110108

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Using a Fuzzy Logic Integrated Machine Learning Algorithm for Information Fusion in Smart Parking

The free flow of people and products within metropolitan areas depends on well-managed transportation systems. However, public parking places in smart cities are often limited by traffic, causing cars and residents to waste time, money, and fuel. To counteract this issue, today's automobile systems combine information fusion with intelligent parking solutions. In this research, we present a Fuzzy Logic Integrated Machine Learning Algorithm (FL-MLA) for use in smart parking and traffic management in a metropolis. The FL-MLA use fuzzy induction to distinguish between parked and moving vehicles while calculating traffic flow. The suggested technique efficiently resolves the problem of locating suitable parking places by avoiding incorrect configurations that govern traffic management difficulties. Therefore, the FL-MLA is used in traffic management systems to boost performance metrics like efficiency ratio (98.1%) and accident detection (98.1%) based on simulation results like reduced energy consumption (95.3%), more accurate traffic estimation (97.9%), higher average daily park occupancy (97.2%), and higher efficiency ratio (98.1%).

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Mohammed Abdul J. Maktoof mail -

Anwar Ja’afar M. Jawad mail -

Hasan M. Abd mail -

Ahmed Husain mail -

Ali Majdi mail

link https://doi.org/10.54216/FPA.110109

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Intelligent Decision Making in IoT-Based Enterprise Management through Fusion Optimization with Deep Learning Models

Because of the proliferation of digital technologies, organizations now have access to previously unimaginable troves of data. In order to make educated choices and generate beneficial results, accurate data analysis and interpretation are essential. The use of data visualization in this context has proven its value. Recent studies found that data visualization increased business owners' drive to make a profit. To aid business owners in evaluating issues related to self-service data resources, a dynamic IoT-based enterprise management framework (IEMF-IDM) was presented. The suggested system uses fusion optimization techniques to maximize the fusion score and enhance decision-making through the use of various models and methods, such as machine learning and fuzzy approaches. Simulation studies in a number of domains, including robots, cloud settings, and multimedia data fusion, attest to the system's efficacy.

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Saif Saad Ahmed mail -

Anwar Ja’afar M. Jawad mail -

Shorook K. Abd mail -

Aymen Mohammed mail -

Amjed Hameed Majeed mail

link https://doi.org/10.54216/FPA.110201

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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Fusion: Practice and Applications | 2023

Enhancing IoT-Based Intelligent Video Surveillance through Multi-Sensor Fusion and Deep Reinforcement Learning

Currenlty, wireless communication that is successful in the Internet of Things (IoT) must be long-lasting and self-sustaining. The integration of machine learning (ML) techniques, including deep learning (DL), has enabled IoT networks to become highly effective and self-sufficient. DL models, such as enhanced DRL (EDRL), have been developed for intelligent video surveillance (IVS) applications. Combining multiple models and optimizing fusion scores can improve fusion system design and decision-making processes. These intelligent systems for information fusion have a wide range of potential applications, including in robotics and cloud environments. Fuzzy approaches and optimization algorithms can be used to improve data fusion in multimedia applications and e-systems. The camera sensor is developing algorithms for mobile edge computing (MEC) that use action-value techniques to instruct system actions through collaborative decision-making optimization. Combining IoT and deep learning technologies to improve the overall performance of apps is a difficult task. With this strategy, designers can increase security, performance, and accuracy by more than 97.24 %, as per research observations.

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Aymen Hussein mail -

S. Ahmed mail -

Shorook K. Abed mail -

Noor Thamer mail

link https://doi.org/10.54216/FPA.110202

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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Fusion: Practice and Applications | 2023

Machine Learning-Based Intelligent Video Surveillance in Smart City Framework

The proposed method of using Machine Learning in Motion Detection and Pedestrian Tracking-assisted Intelligent Video Surveillance Systems (ML-IVSS) can be seen as an application of intelligent fusion techniques. ML-IVSS combines the power of motion detection, pedestrian tracking, and machine learning to create a more accurate and efficient surveillance system for smart cities. By fusing these techniques, ML-IVSS can effectively detect unusual behaviors such as trespassing, interruption, crime, or fall-down, and provide accurate depth data from surveillance footage to protect residents. Intelligent fusion techniques can help improve the accuracy and effectiveness of surveillance systems in smart cities, making them safer and more secure for residents. Combination channel models are used at first, and an object area with prominent features is selected for surveillance. Scaled modification and extraction of features are carried out on the presumed object's region. Identifying the low-level characteristic is the first step in incorporating it into neural architectures for deep feature learning. A smart CCTV data set is used to evaluate the proposed method's performance. According to the numerical analysis, the proposed ML-IVSS model outperforms other traditional approaches in terms of abnormal behaviour detection (98.8%), prediction (97.4%), accuracy (96.9%), F1-score (97.1%), precision (95.6%), and recall (96.2%).

groups

Mohammed A. J. Maktoof mail -

Ibraheem H. M. mail -

Mohammed A. Abdul Razzaq mail -

Ahmed Abbas mail -

Ali Majdi mail

link https://doi.org/10.54216/FPA.110203

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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Fusion: Practice and Applications | 2023

Physical Activity Monitoring for Older Adults through IoT and Wearable Devices: Leveraging Data Fusion Techniques

The emergence of low-cost individual sensing devices has facilitated the application of data fusion methods to yield insights useful for score-level, rank-level, or hybrid-level fusion. Intelligent tools for fusion processing, such as fuzzy methods and optimization algorithms, may be used to the deluge of raw data generated by these devices. The use of numerous sensors allows for multi-level/hybrid-level fusion, and the combination of several models for intelligent systems allows for fusion system design optimized for score improvement. Multimedia data fusion applications and machine learning methods can be used to accomplish data fusion in cloud settings. For older people in independent living conditions, a physical activity assessment framework (PAAF) that uses deep learning models for fusion to identify activity and evaluate progress based on the spectral domain of each window is needed. This study highlights the significance of data fusion in outlining the needs for IoT devices in networked computers for distant patient monitoring. In order to provide for the health of the elderly without compromising their comfort or freedom of choice, we need a seniors network based on the Internet of Things and wearable health technology. The sensors' functionality was investigated by analyzing data gathered from the environment and the organisms within it. The proposed PAAF-IoT architecture has many layers, each one connected to a different device, with the most important part being the integration of data from all of them to classify types of physical activity. Cloud services geographically close to the customer are used to process the resulting mountain of data, reducing end-to-end delay and facilitating prompt responses from healthcare professionals. Data fusion in healthcare and remote patient monitoring are demonstrated through the deployment of an app that allows doctors to remotely administer prescriptions and maintain track of patients' medical histories.

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Hayder Mahmood Salman mail -

Hasan Faleh Hamdan mail -

Raed Khalid mail -

Sanaa Al-Kikani mail

link https://doi.org/10.54216/FPA.110204

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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Fusion: Practice and Applications | 2023

Multi-Level Fusion Optimization in Cyber-Physical Systems Using Computer Vision-Based Fault Detection

The healthcare sector's use of cyber-physical systems to provide high-quality patient treatment highlights the need for sophisticated security solutions due to the wide range of attack surfaces from medical and mobile devices, as well as body sensor nodes. Cyber-physical systems have various processing technologies to choose from, but these technical methods are as varied. Existing technologies are not well-suited for managing complex information about problem identification and diagnosis, which is distinct from technology. To address this issue, intelligent techniques for fusion processing, such as multi-sensor fusion system architectures and fusion optimization, can be used to improve fusion score and decision-making. Additionally, the use of deep learning models and multimedia data fusion applications can help to combine multiple models for intelligent systems and enhance machine learning for data fusion in E-Systems and cloud environments. Fuzzy approaches and optimization algorithms for data fusion can also be applied to robotics and other applications.. In this paper, a computer vision technology-based fault detection (CVT-FD) framework has been suggested for securely sharing healthcare data. When utilizing a trusted device like a mobile phone, end-users can rest assured that their data is secure. Cyber-attack behavior can be predicted using an artificial neural network (ANN), and the analysis of this data can assist healthcare professionals in making decisions. The experimental findings show that the model outperforms with current detection accuracy (98.3%), energy consumption (97.2%), attack prediction (96.6%), efficiency (97.9%), and delay ratios (35.6%) over existing approaches.

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Mustafa Altaee mail -

Anwar Ja’afar M. Jawad mail -

Mohammed A. Jalil mail -

Noor Sami mail -

Zaid Saad Madhi mail

link https://doi.org/10.54216/FPA.110205

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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Journal of Neutrosophic and Fuzzy Systems |

Bipolar neutrosophic soft continuity mappings

In this article, we have introduced a bipolar neutrosophic soft point and investigated some of the properties with appropriate examples. Further, we have defined bipolar neutrosophic soft continuous mapping through bipolar neutrosophic soft points. Some results have been produced as theorems and examples. Further, we have discussed the relationship between the proposed mapping with the various existing mappings.

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Journal of Neutrosophic and Fuzzy Systems |

Bipolar neutrosophic soft continuity mappings

In this article, we have introduced a bipolar neutrosophic soft point and investigated some of the properties with appropriate examples. Further, we have defined bipolar neutrosophic soft continuous mapping through bipolar neutrosophic soft points. Some results have been produced as theorems and examples. Further, we have discussed the relationship between the proposed mapping with the various existing mappings.

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Fusion: Practice and Applications | 2023

Fusion System for Blockchain Asset Securitization Risk Control Using Adaptive Deep Learning-Based Framework

Feature engineering methods, which entail identifying and extracting useful features from big datasets, can be used to enhance the precision of asset securitization. It might be difficult to securitize assets that produce multiple receivables, such as consumer or company debt. In order to overcome these difficulties, companies might think about adopting a fusion system that integrates feature engineering with distributed ledger technologies such as blockchain. Businesses can benefit from implementing a fusion system like the Deep learning-based Adaptive Online Intelligent Framework (DLAOIF) since it allows for better decision-making, less wasted time and money, and less chance of fraud. Financial asset tracking on a blockchain can help investors keep a closer eye on asset performance and related risks, while also decreasing their reliance on credit rating agencies. Blockchain's high data security standards and elimination of regulatory bottlenecks in the securitization process also make it a useful tool for easing the burden of due diligence.  

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Raed Khalid mail -

Omar Saad Ahmed mail -

Talib A. Al-Sharify mail -

Wasfi Hameed mail -

Riyam K. Marjan mail

link https://doi.org/10.54216/FPA.110206

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

Details open_in_new