Machine Learning-Based Intelligent Video Surveillance in Smart City Framework

Mohammed A. J.; Ibraheem H.; Mohammed A. Abdul; Ahmed; Ali

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

American Journal of Business and Operations Research | 2023

Business Process Management and Process Mining Technologies: The progress of a discipline

A wide variety of approaches, strategies, and tools for designing, implementing, managing, and analyzing functional business processes have emerged from studies in business process management (BPM). It is the goal of the emerging topic of research known as "process mining" (PM) to improve the analysis of business process models by gleaning actionable insights from massive quantities of event logs. The purpose of this study is to research business process management and process mining by surveying the state-of-the-art methods and tools in each area and highlighting the most recent developments. This study concludes with a discussion of BPM and PM, in which PM acts as a bridge between BPM and data science to enhance business processes (BPs).

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Samah Ibrahim Abdelaal mail

link https://doi.org/10.54216/AJBOR.100105

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Accurate Recognition of Natural language Using Machine Learning and Feature Fusion Processing

To enhance the performance of Chinese language pronunciation evaluation and speech recognition systems, researchers are focusing on developing intelligent techniques for multilevel fusion processing of data, features, and decisions using deep learning-based computer-aided systems. With a combination of score level, rank level, and hybrid level fusion, as well as fusion optimization and fusion score improvement, these systems can effectively combine multiple models and sensors to improve the accuracy of information fusion. Additionally, intelligent systems for information fusion, including those used in robotics and decision-making, can benefit from techniques such as multimedia data fusion and machine learning for data fusion. Furthermore, optimization algorithms and fuzzy approaches can be applied to data fusion applications in cloud environments and e-systems, while spatial data fusion can be used to enhance the quality of image and feature data In this paper, a new approach has been presented to identify the tonal language in continuous speech. This study proposes the Machine learning-assisted automatic speech recognition framework (ML-ASRF) for Chinese character and language prediction. Our focus is on extracting highly robust features and combining various speech signal sequences of deep models. The experimental results demonstrated that the machine learning neural network recognition rate is considerably higher than that of the conventional speech recognition algorithm, which performs more accurate human-computer interaction and increases the efficiency of determining Chinese language pronunciation accuracy.

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Hayder Mahmood Salman mail -

Vian S. Al-Doori mail -

Hayder sharif mail -

Wasfi Hameed4 mail -

Rusul S. Bader mail

link https://doi.org/10.54216/FPA.100108

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Fusion Processing Techniques and Bio-inspired Algorithm for E-Communication and Knowledge Transfer

This study suggests employing a dynamic natural and bio-inspired algorithm (DNBIA) to strengthen the confidentiality, integrity, and availability of digital information exchanges. You may think of the suggested method as a clever approach to Fusion Processing. Fusion Processing is the practice of combining and analyzing information from many databases. The efficiency and reaction time of e-communication systems may be increased by the use of the suggested DNBIA algorithm, which processes and integrates data from different sources. It is also possible to see the multi-objective optimization study presented in this work as a type of Fusion Processing. Cyberattacks and other types of computer security risks are the focus of this study, which seeks to optimize numerous objectives concurrently in order to eliminate them. The study can give a complete solution to improve the security of e-communication systems by combining different goals. The suggested method of enhancing e-communication and information transmission using DNBIA and multi-objective optimization analysis can be seen as a type of Fusion Processing. Efficient e-communication systems may be achieved by collecting data from a variety of sources and analyzing the results.

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Omar Saad Ahmed mail -

Fay Fadhil mail -

Laith H. Jasim Alzubaidi mail -

Riyadh Al-Obaidi mail

link https://doi.org/10.54216/FPA.100109

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

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Fusion: Practice and Applications | 2023

Text and Social Analytics with Fusion Techniques Enhance Hospital Health Management

the impact of social analytics on hospital health management: a multilevel fusion approach for data-driven decision-making and brand improvement. The hospital health management center should use feature extraction techniques to learn more about customers' feelings towards their services and optimize their business strategies and promotions accordingly. The proposed multi-level/hybrid level fusion system architectures can effectively integrate data/images from multiple sources, including social networks, to collect and process essential data for score level and rank level decision-making. This approach leverages intelligent techniques, such as deep learning models, fuzzy logic, and optimization algorithms, to improve fusion scores and achieve optimal fusion performance. The proposed framework can also be extended to various applications, including multimedia data fusion, e-systems data fusion, and spatial data fusion, to enable intelligent systems for information fusion and decision-making in diverse domains. Therefore, this paper proposes Improved Customer Relation and Business Operations (ICR-BO) to enhance customer relationships in business development using text and social analytics. A case study is carried out to explore the online debate of computer brands operated in hospital environments and Twitter suppliers. The authors used text-mining strategies and social analytics to analyze business operations. Social Media uses data sets to view important observations and trends to identify consumer awareness after collecting critical tweets using Twitter search. The experimental results show that ICR-BO achieves the highest customer relation compared to other existing methods.

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Rana K. A. Ahmed mail -

Ryham Ali Zubaid mail -

Fay Fadhil mail -

Israa Habeeb Naser mail

link https://doi.org/10.54216/FPA.100209

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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Fusion: Practice and Applications | 2023

Multi-Level Fusion for Facial Expression Recognition in Human Behavior Identification

In this study, we present a multi-level fusion of deep learning technique for facial expression identification, with applications spanning the fields of cognitive science, personality development, and the detection and diagnosis of mental health disorders in humans. The suggested approach, named Deep Learning aided Hybridized Face Expression Recognition system (DLFERS), classifies human behavior from a single image frame through the use of feature extraction and a support vector machine. An information classification algorithm is incorporated into the methodology to generate a new fused image consisting of two integrated blocks of eyes and mouth, which are very sensitive to changes in human expression and relevant for interpreting emotional expressions. The Transformation of Invariant Structural Features (TISF) and the Transformation of Invariant Powerful Movement (TIPM) are utilized to extract features in the suggested method's Storage Pack of Features (SPOF). Multiple datasets are used to compare the effectiveness of different neural network algorithms for learning facial expressions. The study's major findings show that the suggested DLFERS approach achieves an overall classification accuracy of 93.96 percent and successfully displays a user's genuine emotions during common computer-based tasks.

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Aqeel Hussein mail -

Ibraheem H. M. mail -

Sarah Ali Abdulkareem mail -

Ryam Ali Zubaid mail -

Noor Thamer mail

link https://doi.org/10.54216/FPA.100210

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

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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

Details open_in_new