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Application of Deep neural network for an Effective user experience in an interactive platform

Application of Deep neural network for an Effective user exp...

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

作者： Fahidhil, Eay Abdulhussain, Zahraa N. Hussam, Ragheed Ghali, Fatima Jameel Mahdi, Hasan Harith Sadiq, Sadiq Nabeel 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-farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq AI-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Nisour University College Computer Engineering Techniques Baghdad Iraq

ISBN: (纸本)9798350303452

The technical community is becoming more concerned with the interface between humans and machines to increase the capability of the real device to fulfill the user's expectations and improve the interpretation of human control as accurately as possible. Intelligent systems that can mold themselves to their users' characteristics need dedicated research and development. A user-controlled, interactive robot visualizer that actively searches for and brings the viewer an item they choose. As a result, this study aims to present a Deep neural network-based interactive platform (DNNIP) to enhance understanding concerning the thinking of the end user. A deep neural network (DNN) and particle filtering may estimate the user location. The robot will first use location data to navigate the user's area, then face recognition to provide service at the user's location. The artificial bee colony (ABC) algorithm is a famous meta-heuristic optimization method based on how honeybees find sustenance. ABC is great at exploring low manipulation ability, low response, and high accuracy in robot interaction. Consequently, the experiments' findings demonstrate that the suggested model is a smart, more effective, and more reliable tool than conventional communication techniques. © 2023 IEEE.

关键词： Deep neural networks

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Application of Wearable Sensors in Physical education for biomedical surveillance and human-machine interface

Application of Wearable Sensors in Physical education for bi...

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

作者： Alsalhy, Mohammed Jameel Ahmed, Ibrahem Hussien, Naseer Ali Hussain, Angham Khalid Hussian, Ahmed Al-Shaikhli, Taha Raad 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 Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar 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

Continuous biological surveillance requires less mechanical aid and affordable wearable technology and sensors. The health industry benefits greatly from the rapid development and expansion of the Internet of Things (IoT). Measuring the accuracy of physical education systems is a major difficulty for fitness and sports trackers, as human dependability is a primary concern for these technologies. This study supports the use of a portable sensor device for monitoring activities in athletes' training. The proposed method is achieved with the help of monitoring physical education using a wearable sensor device(MPE-Wsd)to track their health. With the Service Level Agreement (SLA) method, processing power requirements are estimated with the help of a server. The article opened with a unique and compelling model for health. The article offers a portable sensor for use at the human-machine interface. It's convenient for both the participants and the doctor to have access to the various sources of information. With this new field of study, policymakers may make the most efficient use of hardware and software designed for the Internet of Things. Provides the network with instantaneous access to large-scale resources while reducing operational and capital expenditures. Many mental and physiological diseases are rising as the global population ages because point-of-care (POC) treatment and continuous disease monitoring are important. The results of the tests validate the technique as being both convenient and economical to employ regularly. © 2023 IEEE.

关键词： Internet of things

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Environmental and Financial Asset Optimization through Multi-Restoration Informed by AI Systems for Optimizing Ecosystem Functioning

Environmental and Financial Asset Optimization through Multi...

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

作者： Hasan, Hiba Abdulameer Ettyem, Sajjad Ali Hameed, Waleed Hussien, Naseer Ali Diame, Hussein Alaa Al-Tameemi, Ahmed Raed Computer Technologies Engineering Al-Turath University College Baghdad 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 Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

ISBN: (纸本)9798350303452

Environmental settings study the link between economic growth and resource exploitation because of the need for fair distribution. Economic resources pose difficulties due to the loss of ecosystems, soil deterioration, and long-term toxicity. For enhancing biodiversity, soil deterioration, and toxicity over the long term in environmental environments, the Artificial Intelligence-based Multi-Restoration Ecological Optimization Framework (AI-MEOF) is presented in this research. Reduced reliance on ozone-depleting products, conservative electricity usage, and lower service prices are the results of using resource surface prediction analysis. By lowering the chance of being exposed to possibly dangerous chemicals in the environment, AI-MEOF incorporates probabilistic diversity analysis. The study provides an empirical foundation for decision-making to preserve resource-dependent economies' long-term health, stability, and reasonableness. Analysis of the suggested framework through simulation demonstrates its dependability concerning sensitiveness, achievement, and efficiency. © 2023 IEEE.

关键词： Restoration

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Investigation of Big Data for Human Emotion Detection using Deep Learning 1

Investigation of Big Data for Human Emotion Detection using ...

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

作者： Ali Hussien, Naseer Shnawa, Ammar H. Jaffer, Zain Wasmi Zaydan, Nabaa Rabea Hussam, Ragheed Noori, Mohammed Al-Ayen University Information and Communication Technology Research Group Scientific Research Center Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Turath University College Computer Technologies Engineering Baghdad Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

The significance of this study relies upon inside the numerous fields it has the potential to impact positively;these fields consist of human-computer interaction, medicinal drug, advertising and marketing, and academia. The study intends to improve user experience, targeted actions, and make a contribution to the creation of emotionally wise structures by deciphering complex emotional states within extensive datasets. Data privacy issues, algorithmic biases, and the inaccessibility of deep mastering models represent merely some of the problems that the advised Human Emotion Detection technique seeks to solve. To correctly negotiate moral concerns and assure sturdy emotion detection in a huge variety of situations, Human Emotion Detection (HED) prioritizes a balance among model complexity and transparency. The counseled technique of Deep Learning-assisted Robust Human Emotion Detection (DL-RHED) makes use of sophisticated deep learning architectures, which includes attention mechanisms and recurrent neural networks, to hit upon nuances in expression over time. This technique seeks to offer an all-encompassing information of human emotions, permitting the version to dynamically adjust to new emotional displays. Validation of the proposed DL-RHED approach using simulation analysis is a fundamental a part of this research. The effectiveness and consistency of DL-RHED across distinct datasets are established inside the conceptual, which incorporates details about the design of the experiment, evaluation measures, and simulation consequences. These investigations provide real proof for the practicality and scalability of the proposed method. © 2023 IEEE.

关键词： Big data

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Strategy and effect factors for a flexible financial infrastructure and supply chain

Strategy and effect factors for a flexible financial infrast...

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

作者： Hameed, Waleed Alkhafaji, Mohammed Ayad Ghali, Fatima Kalaw, Haider Jaffer, Zain Abdulhasan, Maki Mahdi Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar 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

In an unstable market, supply chain management (SCM) must use novel approaches to meet consumers' expectations better. Supply chains must become more adaptable to cope with the disturbance that calls for increased supply chain resilience. This fresh perspective posed the difficulty of creating and overseeing a supply chain network that cultivates interdependent links via purposeful collaboration. This research proposes a Customer Support Prediction Model (CSPM) to improve the efficiency and competitiveness of the supply chain. The effect of these actions on supply chain performance should be tracked using operational and economic efficiency metrics. The impact of the proposed agile and resilient techniques on rivalry in the supply chain is studied concerning time to market, product effectiveness, and customer satisfaction. This study compiles a set of uniformly accurate and trustworthy metrics through iterative measurement analysis. Theory development in SCM is facilitated by the suggested CRPM, which may be applied in various settings to improve existing conceptualization and measurement procedures and assess a wide range of theoretical models. © 2023 IEEE.

关键词： Supply chain management

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Multi-agent robotics system with whale optimizer as a multi-objective problem

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9637-9649页

作者： Gul, Faiza Mir, Imran Mir, Suleman Abualigah, Laith Electrical Department Air University Aerospace and Aviation Campus Kamra Pakistan School of Avionics and Electrical Engineering College of Aeronautical Engineering National University of Sciences and Technology Risalpur Pakistan Electrical Department Fast-National University of Computer and Emerging Sciences Peshawar Pakistan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan College of Engineering Yuan Ze University Taoyuan Taiwan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

A multi-objective optimization method for exploring uncharted space is presented in the study. Robotics exploration personnel create a map of the immediate area using sensor data. It has been observed that a single optimization technique is typically used with a specific objective function to carry out the optimization for space exploration. The optimization process can be sped up and simplified using a mono-objective function, but the map accuracy and exploration depth suffer. This work offers an optimization technique with improved multi-objective functions in recognition of this crucial factor. This not only speeds up the search procedure but also improves the precision of the maps. The Reconfigured Whale Algorithm (rWO), the suggested framework, is based on a Whale Optimizer inspired by whales’ biological behavior. Initializing the whale population, also known as waypoints, is the first step. After being established in the initial stage/iterations, these waypoints are considered constant. The location update from the robot-catered non-dominated waypoints is the next phase. The algorithm optimizes the waypoints. Extensive simulations that simulate various scenarios and environments are used to assess the performance matrices properly. Following the identification of the algorithm’s advantages, the effectiveness of the outcomes is demonstrated by contrasting its performance with that of three popular optimization algorithms: the hybrid CME-Whale Optimizer (WO), the Coordinated Multi-Robot Exploration (CME), and the Arithmetic Optimizer (AO) integrated with CME. Findings show that the suggested approach significantly improves the optimization process by expanding the area that is investigated and speeding up the search process. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Multiobjective optimization

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Prevention and Management of Natural Disasters with Integrated Multidisciplinary Climatic Sensitivity and Big Data Model 1

Prevention and Management of Natural Disasters with Integrat...

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

作者： Hussian, Ahmed Hussien, Naseer Ali Al-Chilibi, Haider. Alsalhy, Mohammed Jameel Abdul Jaleel Maktoof, Mohammed Matti, Imad Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Ayen University Information and Communication Technology Research Group Scientific Research Center Thi-Qar 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

The comprehension and predictability of seasonal, yearly, decadal, and hundred-year climatic performances are improved by using climate models. Models observe the level of climatic change attributable to natural variability, human activities, or a combination of both. The problematic aspects of climatic considerations include heat-related disease and death, rising industrial losses, shortages, and damaged infrastructure, which are crucial components. To sustain their way of life, an Integrated Multidisciplinary Climatic Sensitivity utilizing Big Data Model (IMCSBDM) has been suggested in this article, which would exacerbate societal issues like hunger and poverty. Research on social and climate issues has been carried out to gather information on the climate, environment, and social interactions. A superposed epoch resolution analysis is made to provide trading grounds with adaptive potential. In this suggested work, the analytical test of the simulation is carried out based on security, effectiveness, and anonymity. © 2023 IEEE.

关键词： Big data

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Moving Scene Classification And Human Action Recognition Based On Artificial Intelligence 1

Moving Scene Classification And Human Action Recognition Bas...

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

作者： Hassan, Ahmed R. Taha, Ahmed Hussien, Naseer Ali Khalid Hussain, Angham Ali, Aqeel Matti, Imad National University of Science and Technology Engineering Technologies Department of Medical Devices Nasiriyah Dhi Qar Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Ayen University Information and Communication Technology Research Group 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 Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

In friendly correspondence, human behavior was generally a basic part. All signs that permit an assessment of the human way of behaving are human action and activity acknowledgment. Recognizing human action is a huge issue in different applications, including human-PC cooperation and innovative video observing, to further develop security in many fields. Often, recognizing human exercises with the single perception of the human body and the elements of the climate and the related items is conceivable. Look at this subject and proposition as a system for distinguishing human action that incorporates various utilitarian channels from different things, like items, views, and individuals. This paper sums up current headways in acknowledgment of human movement. By and large, distinguishing human exercises with the single perception of the human body and the highlights of the climate and comparing items is conceivable. This study offers artificial intelligence-based human action recognition (AI-HAR). Given the rising notoriety and late forward leaps in human conduct acknowledgment because of manufactured reasoning, the review gives information on the latest things and possible core values for future logical endeavors. © 2023 IEEE.

关键词： Behavioral research

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LET-Net: locally enhanced transformer network for medical image segmentation

LET-Net: locally enhanced transformer network for medical im...

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作者： Ta, Na Chen, Haipeng Liu, Xianzhu Jin, Nuo College of Computer Science and Technology Jilin University Changchun130012 China College of Computer Hulunbuir University Hulunbuir021008 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and Technology Changchun130022 China Southampton Business School University of Southampton SouthamptonSO17 1BJ United Kingdom

Medical image segmentation has attracted increasing attention due to its practical clinical requirements. However, the prevalence of small targets still poses great challenges for accurate segmentation. In this paper, we propose a novel locally enhanced transformer network (LET-Net) that combines the strengths of transformer and convolution to address this issue. LET-Net utilizes a pyramid vision transformer as its encoder and is further equipped with two novel modules to learn more powerful feature representation. Specifically, we design a feature-aligned local enhancement module, which encourages discriminative local feature learning on the condition of adjacent-level feature alignment. Moreover, to effectively recover high-resolution spatial information, we apply a newly designed progressive local-induced decoder. This decoder contains three cascaded local reconstruction and refinement modules that dynamically guide the upsampling of high-level features by their adaptive reconstruction kernels and further enhance feature representation through a split-attention mechanism. Additionally, to address the severe pixel imbalance for small targets, we design a mutual information loss that maximizes task-relevant information while eliminating task-irrelevant noises. Experimental results demonstrate that our LET-Net provides more effective support for small target segmentation and achieves state-of-the-art performance in polyp and breast lesion segmentation tasks. © 2023, The Author(s).

关键词： Image enhancement

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Efficient learning of continuous-variable quantum states

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Physical Review Research 2024年第3期6卷 033280页

作者： Ya-Dong Wu Yan Zhu Giulio Chiribella Nana Liu John Hopcroft Center for Computer Science QICI Quantum Information and Computation Initiative Department of Computer Science The Department of Computer Science for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada Institute of Natural Sciences Ministry of Education Key Laboratory in Scientific and Engineering Computing School of Mathematical Sciences University of Michigan-

The characterization of continuous-variable quantum states is crucial for applications in quantum communication, sensing, simulation, and computing. However, a full characterization of multimode quantum states requires a number of experiments that grows exponentially with the number of modes. Here we propose an alternative approach where the goal is not to reconstruct the full quantum state, but rather to estimate its characteristic function at a given set of points. For multimode states with reflection symmetry, we show that the characteristic function at M points can be estimated using only O(logM) copies of the state, independently of the number of modes. When the characteristic function is known to be positive, as in the case of squeezed vacuum states, the estimation is achieved by an experimentally friendly setup using only beamsplitters and homodyne measurements.

关键词： Machine learning Quantum information processing with continuous variables Quantum tomography

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