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Target Detection in Complex Environments using UAV and Deep Learning-based Focal Pruning Method 1

Target Detection in Complex Environments using UAV and Deep ...

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1st IEEE International Conference on Emerging Research in computational science, ICERCS 2023

作者： Thejeel, Sara H. Hussian, Ahmed Hussian, Ahmed Ahmed, Wasan Saad Ali Zearah, Sajad Ali, Mohsin National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq AlTurath University College Computer Technologies Engineering Baghdad Iraq Al-Ayen University Scientific Research Center Thi-Qar Iraq Computer Technology Engineering Al-Maamoun University College Baghdad Iraq

ISBN: (纸本)9798350359756

Applications from computer vision that use deep learning for target detection have become incredibly popular recently. The requirement for future applications includes monitoring, visual navigation, object identification, and sensor-based preventative planning, together with the growing UAV industry advances, which hold great potential for deep learning. Deep learning techniques for object detection have swiftly evolved into a rendering tool for photos taken by flying drones. This paper's primary goal is to present a comprehensive overview of contemporary deep learning-based object recognition algorithms and to explore their most recent contributions to UAV low-altitude datasets. As opposed to standard or remote-sensor datasets, which comprise a smaller percentage of the literature, low-level UAV datasets are the focus of the research. To conduct real-time bridge crack diagnosis with Unmanned Aerial Vehicles (UAV), a deep learning system dubbed the You Only Look Once version-4 Focal Pruning Method (YOLOv4-FPM) is suggested based on the YOLOv4 model. For YOLOv4-FPM, focal loss architecture is utilized to streamline the loss feature, improve accuracy and colour transmission, and solve issues with dynamic context. A pruning method is used to speed up detection and facilitate the network. The outcomes show that the model can successfully handle the difficulty given by various backdrops, and the detection velocity is 97.2% effective enough for the real-time detection mission. The YOLOv4-FPM of deep learning technology guarantees 96.3% detection accuracy compared to current drone detection methods. © 2023 IEEE.

关键词： Object recognition

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Internet of Things and wearable device-basedtracking of an elderly person's physical activity 1

Internet of Things and wearable device-basedtracking of an e...

引用

1st IEEE International Conference on Emerging Research in computational science, ICERCS 2023

作者： Ali Zearah, Sajad Mohammed Abed, Ansam Hussian, Ahmed Ali Ettyem, Sajjad Hussam, Ragheed Matti, Imad Alayen University Scientific Research Center Thi-Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Technologies National University of Science and Technology Department of Medical Devices Engineering Nasiriyah Dhi Qar Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

The potential for real-time activity tracking (R- TAT) to shed light on the health and habits of the elderly is a major reason why it should be widely adopted. This strategy allows for the unobtrusive collecting and analysis of data connected to a person's day-to-day physical activities by deploying wearable devices equipped with sensors and leveraging the connectivity of IoT. These details can be crucial in the early diagnosis of health problems, allowing for prompt interventions and individualized healthcare plans. Ethical constraints, data privacy, and the requirement for userfriendly gadgets offer considerable challenges to mainstream adoption. To facilitate real-time monitoring while giving top priority to data integrity and confidentiality, a new algorithm called the IoT-Enabled Data Transmission Privacy Recognition Algorithm (IoT-DTPRA) has been suggested in the present research. Provides fine-grained control over data sharing options and uses strong measures like encryption and anonymization to preserve the privacy of senior citizens' personal information. Caretakers, healthcare professionals, and family members all benefit from the actionable information provided by IoT-DTPRA since it provides a comprehensive and privacy-conscious way to monitoring the physical activity of the elderly. The suggested methodology is verified using simulation analysis, which use actual situations and synthetic datasets to assess the IoT-DTPRA system's performance in a range of environments. The methodology's reliability and robustness across a wide range of physical activities and environmental circumstances have been verified by this thorough validation. © 2023 IEEE.

关键词： Internet of things

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Application of Internet of Things and Cognitive Design Protocol for Clothing Pattern Design

Application of Internet of Things and Cognitive Design Proto...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Zearah, Sajad Ali Abdul Kadeem, Sahar R. Taha, Ahmed Mahdi Hussin, Marwa Saad Majeed, Maryam Ghassan Al-Tameemi, Ahmed Raed Scientific Research Center Al-ayen University Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

ISBN: (纸本)9798350303452

Designing software is used regularly in most fabric firms to create prototypes of new garments. The study suggests a deep-learning-based IoT-based computational modeling protocol for fashion pattern creation. For this new protocol, the computerized design algorithm relies on a Deep Learning-based Imaging Model (DLIM). Employing only 2D forms, the designer can create any pattern they like. To help choose the right hue for projects, DLIM provides palettes of various hues. DLIM interprets the concept's pattern, colors, and quantity for automated clothing production and generates a code that the machine can read. The designs have been uploaded at various locations on the IoT network, and this information is compiled into a database for later recognition by DLIM. The designer can go beyond their creativity with the help of DLIM and a computational development protocol. Simulations of this technology have shown that it reduces the mean square error rate of the IoT platform while still providing a straightforward interface and pleasant experience for customers. Therefore, the computational designing protocol for IoT platforms may deliver maximum performance (98.93%) with minimal mistakes. © 2023 IEEE.

关键词： Deep learning

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Workplace Safety Training for Healthcare Workers Based on Deep Learning

Workplace Safety Training for Healthcare Workers Based on De...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Adile, Mohaned Kadeem, Sahar R. Abdul Zearah, Sajad Ali Abdulkareem, Sarah Ali Fahidhil, Eay Abbas, Jamal K. Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Scientific Research Center Al-ayen University Thi-Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Nisour University College Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350303452

Training and education for medical staff is crucial to ensuring patients get the best possible treatment. Healthcare is and always has been a very technical and specialized industry. Maintaining a competent workforce and a healthy work-life balance depends on the program's effectiveness. This article details nurses' perspectives on patient safety and examines the frequency of contrasting incidents. This paper proposes the Deep learning-assisted statistical Model (DLASM) to better teach healthcare workers in safety practices. Those in charge of employee safety may benefit from data gathered and analyzed by AI automation. The financial burden of workplace events, injuries, healthcare, and prosecutions might be reduced by implementing an appropriate exercise program based on a meaningful learning model for employees. DLASM technique will lead to happier, safer, and healthier employees. The experimental findings demonstrate that the suggested DLASM technique improves efficiency, accuracy, and predictability compared to existing approaches © 2023 IEEE.

关键词： Deep learning

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Machine Learning-Aided Optimization Approach for Sustainable Energy Systems 1

Machine Learning-Aided Optimization Approach for Sustainable...

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1st IEEE International Conference on Emerging Research in computational science, ICERCS 2023

作者： Hussian, Ahmed Ali Ettyem, Sajjad Ali Zearah, Sajad Ahmed, Ibrahem Ismail, Laith S. Ibrahem, Marwa Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq National University of Science and Technology Dhi Qar Department of Medical Devices Engineering Technologies Nasiriyah Iraq Al-Ayen University Thi-Qar Scientific Research Center Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

Energy derived from renewable sources is expanding quickly. Renewable energy systems meet off-grid and on-grid load requirements, often connected to conventional sources. Geothermal, solar, wind, and other renewable energy sources can be harnessed economically at the current rate of technological development. Renewable-based electricity production is an alternative form of green power generation that reduces issues with energy consumption. Though this raises numerous uncertainties and difficulties, it may jeopardize potential economic, technological, and environmental gains. Consequently, a Machine Learning aided Optimization Approach (MLOA) for sustainable energy systems has been suggested in this study. The Enhanced Long Short-Term Memory (E-LSTM) model of recurrent neural networks trained by Adam optimizer is employed in MLOA due to its ability to predict solar and wind generation to provide optimal energy consumption control. The numerical outcomes demonstrate that the suggested MLOA method improves prediction and accuracy rates by 98.22% compared to other current approaches. © 2023 IEEE.

关键词： Energy utilization

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Deep reinforcement learning-based Smart Education System with wireless sensor network virtualization

Deep reinforcement learning-based Smart Education System wit...

引用

2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Abdulkareem, Sarah Ali Jabbar, Kadim A. Ahmed, Ibrahem Zearah, Sajad Ali Diame, Hussein Alaa Sadiq, Sadiq Nabeel Al- Turath University College Computer Technologies Engineering Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Scientific Research Center Al-ayen University Thi-Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Nisour University College Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350303452

Create a smart classroom that uses many sensors, such as webcams, to monitor each student's progress and leverages deep reinforcement learning (DRL) algorithms to provide personalized instruction based on each student's unique sensing data. Smart learning suggestion systems also analyze students' pulse rates, quiz scores, and expressions to deduce their current levels of comprehension. They then use DRL to provide suggestions for pupils based on where they are in their studies. It is beneficial to employ virtual reality in the classroom. Software virtualization technologies are now the main focus of IT and systems management publications. Virtualization technology might make service upkeep simpler. Changes to the classroom setting, the teaching strategy, and individual student conditions may drastically alter the efficacy of a conventional classroom. A smarter way to manage the classroom that takes context into account based on wireless sensor network (WSN) technology utilizing a virtualization process with DRL (WV-DRL) is suggested and implemented in this research to improve learning efficiency. Wireless sensors monitor students and classroom conditions, sending data to a server-based management system. The system provides feedback to both students and instructors, as well as wireless sensor-controlled classroom equipment. It uses virtualization technology to make the most of expensive classroom equipment and ensure its reliability. Smart classrooms with several sensors may model a Markov decision process to provide personalized learning suggestions. Preliminary findings indicate that our intelligent learning recommendation system is effective. This research provides useful guidelines for doing so in the future, paving the way for developing an intelligent learning environment that facilitates higher levels of personalization. The experimental results show the proposed WV-DRL to achieve an accuracy ratio of 90.2%, recall ratio of 92.3%, score query 4.7%, the f-scor

关键词： Virtualization

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Research on Corporate Protection Systems using Advanced Protection Techniques and Information Security 1

Research on Corporate Protection Systems using Advanced Prot...

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1st IEEE International Conference on Emerging Research in computational science, ICERCS 2023

作者： Raad, Rabei Zearah, Sajad Ali Saleem, Munqith. Al-Tahee, Ali Mustafa Qasim Abbas, Saad Kadhim, Mohammed National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Iraq Al-Ayen University Scientific Research Center Thi-Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Farahidi University Medical Instruments Engineering Techniques Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

The purpose of a Corporate Protection System (CPS) is to prevent unauthorized individuals from gaining access to a company's facilities and/or data. Based on the nature of the company being secured, particular characteristics and functionalities of a safety measure may need to be tailored to meet their needs. Genetic linkage, selection, recombination rates, mutation rates, genetic drift, mating systems, and population structure influence linkage heterogeneity. Security in business is a technique for protecting business data against unauthorized access and risks that protects company data stored in the system from malicious intent and defends against host-based attacks. Information security suggests that all attempts to safeguard an organization's information are driven by creating resources that might improve the organization's performance. The article proposed an Information Security in Business based Advanced Protection (ISB-AP) technique to safeguard a corporate company's property and resources from intruders. To defend against threats, physical assets, information systems, and other assets are covered by various security procedures based on Artificial Intelligence (AI) technology. The conceptual model of software used in the study incorporates conscientiousness and agreement. Although conscientiousness and agreeableness diminished this link, the behavioural purpose was related to attitude constructs' perceived usefulness and ease of use. The proposed method yields the following results: time in-device ratio of 75.67%, security requirements ratio of 90.955%, transmission bytes in attack ratio of 87.05%, challenges in supply chain ratio of 85.45%, annual occurrence ratio of 89.88%, and overall percentage performance ratio of 91.87%. © 2023 IEEE.

关键词： Supply chains

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Online Student Assessment Based on Facial Expression Recognition through Machine Learning

Online Student Assessment Based on Facial Expression Recogni...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Majed, Safa Al-Hameed, Mazin Riyadh Hussian, Ahmed Zearah, Sajad Ali Nazar Dawood, Mustafa Abbas, Jamal K. Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq Scientific Research Center Al-ayen University Thi-Qar Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

ISBN: (纸本)9798350303452

Engagement is a major aspect of smart teaching since it reveals how much students are learning. Compared to traditional face-to-face classrooms, online courses lack exact, quick, and relevant communication and feedback between lecturers and students. Existing data structures for automated student interaction are geared at analyzing participation, which is crucial to the interface's ability to provide appropriate feedback. Students may struggle with data structures and algorithms due to their novelty, yet the topic is important because of the strategic model teaching framework that includes automated teaching of data structures via group-based collaborative interactions among peers. This paper suggests the Novel Facial Expression Recognition Model (NFERM) based on Machine Learning to analyze student engagement and evaluate student assessment in online education. Students are 'engaged' when they maintain high attention, curiosity, enthusiasm, optimism, and passion for what they are taught or learning. Evaluating students' progress in an eLearning course is best done via activities including quizzes, matching tests, self-assessments, studies, questions for issue resolution, questions based on scenarios, and games. To better identify interactions in photos, this research offers a machine learning approach that addresses the issue of data imbalance by first training on publicly accessible facial expression data. This research presents a virtual computer's-eye-view of a proposed architecture for incorporating facial expression recognition (FER) algorithm into online learning platforms. © 2023 IEEE.

关键词： Students

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Smart City Application based on Internet-of-Things and Artificial Intelligence for Energy Management Dynamic Memory Modelling 1

Smart City Application based on Internet-of-Things and Artif...

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1st IEEE International Conference on Emerging Research in computational science, ICERCS 2023

作者： Zearah, Sajad Ali Al-Tahee, Mustafa Mohammed, Aymen Shnawa, Ammar H. Harith Jameel Mahdi, Hasan Majeed, Zainab Al-Ayen University Scientific Research Center Thi-Qar Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

By and by, the world ingests a lot of energy, including power, machine protection energies, water-driven energy, etc. Since power stays huge, considering world development, a few researchers have endeavored to deliver power from environmentally friendly power sources from intelligent gadget sensor's point of interaction to address family and modern energy deficiencies. To address the energy management problem during information processing on the Internet of Things (IoT) platform, an Artificial Intelligence Assisted Dynamic Memory Modelling on Internet-of-Things (AIADMM) has been designed and developed with mathematical modeling with multiple information processing sensors for domestic and industrial applications. However, These Particular gadgets, for example, the piezoelectric module, electric structure intensity to the electrical converter, and sun-powered board approach, are utilized to associate the energy stockpiling framework for power age in the standard techniques without streamlined calculations, prompting appeal in energy enhancement research. Thus, the whole energy age by environmentally friendly power sources is improved by two models planned in this exploration: the Artificial Neural Network (ANN) and the Artificial Intelligence Assisted Dynamic Memory Modelling on Internet-of-Things (AIADMM). Test results at the lab scale have been broken down in light of Factual constraints, such as Root Mean Square Error (RMSE). The R2 relationship coefficient, dependability factor, energy then, at that point, network clog, and time delay on the IoT stage shows promising outcomes. © 2023 IEEE.

关键词： Internet of things

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Deep Learning with Wireless Sensor Network Platform for Multimedia Data Modeling

Deep Learning with Wireless Sensor Network Platform for Mult...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Mahdi Hussin, Marwa Saad Al-Hameed, Mazin Riyadh Al-Tahee, Mustafa Zearah, Sajad Ali Diame, Hussein Alaa Al-Tameemi, Ahmed Raed Al-Turath University College Computer Technologies Engineering Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-ayen University Scientific Research Center Thi-Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Nisour University College Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350303452

Recent years have seen a meteoric rise in the volume of multimedia data generated by industries like e-commerce, finance, transportation, and social networks. Furthermore, the purpose of wireless multimedia sensor networks (WMSN) is to offer real-time multimedia applications using wireless sensors. Furthermore, Deep learning has an enormous impact on many applications involving multimedia data for tasks, including picture clustering, image classification, natural language processing, and speech recognition. Multimedia data accuracy, WMSN noise, and unattended situations are all problems for traditional methods. There are many moving parts in a successful organization, but network effectiveness is the backbone of an online business. To combat the problem of data noise in wireless sensor networks, the authors of this article propose using Deep Learning to aid in the development of Effective Multimedia Data Models (DLA-EMDM). In particular, no previous work has tried to model multimedia data cyclically using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). Based on the simulation findings, it is clear that the proposed DLA-EMDM approach improves the precision, efficiency ratio, cost-effectiveness, and silence of multimedia data processing in a WSN. By enhancing the aforementioned wireless network, we may furnish enhanced technical support and platform services to businesses and set the stage for enhancing business performance. © 2023 IEEE.

关键词： Wireless sensor networks

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