Fusion: Practice and Applications

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2692-4048ISSN (Online) 2770-0070ISSN (Print)

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

Saif Saad Ahmed , Anwar Ja’afar M. Jawad , Shorook K. Abd , Aymen Mohammed , Amjed Hameed Majeed

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

Vol. 11 Issue. 2 PP. 08-20, (2023)

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

Aymen Hussein , S. Ahmed , Shorook K. Abed , Noor Thamer

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

Vol. 11 Issue. 2 PP. 21-34, (2023)

Machine Learning-Based Intelligent Video Surveillance in Smart City Framework

Mohammed A. J. Maktoof , Ibraheem H. M. , Mohammed A. Abdul Razzaq , Ahmed Abbas , Ali Majdi

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%).

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

Vol. 11 Issue. 2 PP. 35-47, (2023)

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

Hayder Mahmood Salman , Hasan Faleh Hamdan , Raed Khalid , Sanaa Al-Kikani

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

Vol. 11 Issue. 2 PP. 48-61, (2023)

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

Mustafa Altaee , Anwar Ja’afar M. Jawad , Mohammed A. Jalil , Noor Sami , Zaid Saad Madhi

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

Vol. 11 Issue. 2 PP. 62-75, (2023)

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

Raed Khalid , Omar Saad Ahmed , Talib A. Al-Sharify , Wasfi Hameed , Riyam K. Marjan

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

Vol. 11 Issue. 2 PP. 76-89, (2023)