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38th IEEE Region 10 Conference, TENCON 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350302196

Human action identification is a practical area of study with broad applicability in various domains, such as medical care, sport science, and manufacturing management. In logistics, it is essential to identify and examine individual actions, enabling machines to perceive and comprehend human motions for non-verbal interaction. This study specifically fo-cuses on efficiently classifying working activities in the logistics industry using wearable sensors, particularly in the context of human activity recognition. To achieve the research objective, a deep residual neural network was introduced, integrating convolutional layers, shortcut connections, and aggregated transformation for human activity recognition in logistics. The authors evaluated the effectiveness of their proposed deep learning model using the publicly accessible LARa dataset. The LARa dataset comprises a diverse range of human actions in the logistics domain, including standing, walking, cart handling, and synchronization. The details of activity were captured using wearable sensors affixed to different anatomical sites of the study participants. The experimental findings indicate that the model achieved a maximum F-measure of 85.30%. © 2023 IEEE.

关键词： Wearable sensors

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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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Application of AI and ML in Wound Healing and Skin Regeneration

Application of AI and ML in Wound Healing and Skin Regenerat...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Taniya G. Petkar Praveen Kumar Kirtiksha U. Sarate Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Sawangi Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligence-controlled tools improve wound assessment, anticipate healing, and collaborate with regular examinations using cutting-edge dressings and imaging technologies. AI computations work on the management of express data to enhance treatment systems, while advancements in nanomaterials like marine collagen and therapies employing young microorganisms further accelerate skin recovery. Despite encouraging findings, there are still problems, such as potential overfitting and dataset limitations. In general, machine learning is changing the way that injuries are treated by providing proactive, tailored solutions that aim to understand outcomes and lower the cost associated with clinical outcomes.

关键词： Sentiment analysis Medical treatment Wounds Predictive models Prediction algorithms Skin Real-time systems Sensors Monitoring Overfitting

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A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors 7

A Hybrid Deep Neural Network with Attention Mechanism for Hu...

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7th International Conference on Information Technology, InCIT 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350358698

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual's behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained. © 2023 IEEE.

关键词： Smartphones

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Optimizing Patient Outcomes with AI and Predictive Analytics in Healthcare 65

Optimizing Patient Outcomes with AI and Predictive Analytics...

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65th IEEE Annual International Scientific Conference on Power and Electrical engineering of Riga Technical University, RTUCON 2024

作者： Janjua, Jamshaid Iqbal Ghazal, Taher M. Abushiba, Walid Abbas, Sagheer Lahore Pakistan College of Arts & Science Applied Science University P.O.Box 5055 Manama Bahrain Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia College of Engineering Applied Science University Bahrain PMU Department of Computer Science Al Khobar Saudi Arabia

ISBN: (纸本)9798350365771

Healthcare's integration with the emerging artificial intelligence, predictive analytics and health information exchange (HIE) system is currently undergoing a revolution. These emerging technologies are offering better diagnostic accuracies, more personalized treatments, and better patients' outcomes. Artificial Intelligence supported by data from either Electronic Health Records or other big data sources are increasing the accuracy of forecasting critical patient conditions such as disease developments, hospital readmission rates, and mortality risks in patients. These AI-empowered models ensure seamless data sharing between the care providers and promote operational interoperability. Challenges to overcome include issues of privacy of the data, the biases of the prediction algorithms, and the proper integration of the model into the currently established operational environments of healthcare providers. With these technologies combined, it will become possible to optimize patient's outcomes with the provision of data to empower real-time medical interventions. © 2024 IEEE.

关键词： Electronic health record

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Hyperspectral Image Classification Based On 3d Sharpened Cosine Similarity Operation

Hyperspectral Image Classification Based On 3d Sharpened Cos...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xin Qiao Hongjing Wu Swalpa Kumar Roy Weimin Huang Faculty of Engineering and Applied Science Memorial University of Newfoundland Canada Department of Computer Science and Engineering Jalpaiguri Government Engineering College India

Hyperspectral remote sensing images contain abundant spectral information and have a broad application in various areas, such as precision agriculture and geological exploration. However, due to the existence of many bands and the correlation between these bands, it is easy to cause the curse of dimensionality. Therefore, how to efficiently extract spectral and spatial features should be addressed. In this paper, a novel and efficient sharpened cosine similarity operation is applied in hyperspectral images classification to enhance the ability of feature extraction. To validate the efficiency of the proposed method, experiments are conducted on a real hyperspectral dataset, e.g., the University of Pavia (UP). Quantitative and qualitative results demonstrate that sharpened cosine distance operation can efficiently extract discriminative features and achieve a better classification performance.

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A Dynamic Resource-Aware Routing Protocol in Resource-Constrained Opportunistic Networks

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computers, Materials & Continua 2022年第2期70卷 4147-4167页

作者： Aref Hassan Kurd Ali Halikul Lenando Slim Chaoui Mohamad Alrfaay Medhat A.Tawfeek Department of Computer Systems and Communication Technologies Faculty of Computer Science and Information TechnologyUniversiti Malaysia Sarawak(UNIMAS)Kota Samarahan94300Malaysia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka2014KSA Unit-Lab of Sciences of Electronics Technologies of Information and TelecommunicationsSfax UniversitySfaxTunisia Department of Basic Sciences First Common Year DeanshipJouf UniversitySakaka2014KSA Department of Computer Sciences College of Computer and Information SciencesJouf UniversitySakaka2014KSA Department of Computer Sciences Faculty of Computers and InformationMenoufia UniversityEgypt

Recently,Opportunistic Networks(OppNets)are considered to be one of the most attractive developments of Mobile Ad Hoc Networks that have arisen thanks to the development of intelligent *** are characterized by a rough and dynamic topology as well as unpredictable contacts and contact *** is forwarded and stored in intermediate nodes until the next opportunity ***,achieving a high delivery ratio in OppNets is a challenging *** is imperative that any routing protocol use network resources,as far as they are available,in order to achieve higher network *** this article,we introduce the Resource-Aware Routing(ReAR)protocol which dynamically controls the buffer usage with the aim of balancing the load in resource-constrained,stateless and non-social *** ReAR protocol invokes our recently introduced mutual informationbased weighting approach to estimate the impact of the buffer size on the network performance and ultimately to regulate the buffer consumption in real *** proposed routing protocol is proofed conceptually and simulated using the Opportunistic Network Environment *** show that the ReAR protocol outperforms a set of well-known routing protocols such as EBR,Epidemic MaxProp,energy-aware Spray and Wait and energy-aware PRoPHETin terms of message delivery ratio and overhead ratio.

关键词： Opportunistic networks mobile ad hoc networks routing protocols resource-constrained networks load balancing buffer management

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Survey On Recognition Hand Gesture By Using Data Mining Algorithms 4

Survey On Recognition Hand Gesture By Using Data Mining Algo...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Khalaf, Luay Ibrahim Aswad, Salma Abdullah Ahmed, Saadaldeen Rashid Makki, Bassma Ahmed, Mohammed Rashid Tikrit University Faculty of Computer Science and Math Computer Science Department Tikrit Iraq Duhok University Faculty of Administration and Economics Duhok Iraq Karabuk University Computer Science University of Tikrit Karabuk Turkey Al-Esraa University College Department of Computer Technology Engineering Baghdad Iraq Karabuk University Computer Science Karabuk Turkey

ISBN: (纸本)9781665468350

We investigated the usage of hand motion recognition using data mining approaches. In gesture detection applications, I've discovered that employing data mining to recognize hand motions has several advantages. To begin, we employ data mining to recognize bandwidth hand motions. Second, we have a high temporal resolution, which is great for data mining. A short-range, close-pulse broadcast signal is required in most cases. The frequency components of the data mining for hand gesture recognition are the same as the pulse, but the phase components are different. Our vision must be strong enough to differentiate a far hand from distant things. Data mining is equal to recognizing hand motions by heart rate divided by length in these categorized articles. Obtaining significant power for a brief period. © 2022 IEEE.

关键词： Data mining

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IoT Based Systems and Services: Recent Security Concerns and Feasible Solutions

IoT Based Systems and Services: Recent Security Concerns and...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Hafiz Muhammad Attaullah Shahzad Memon Ozlem Feyza Erkan Ruba Khawar Department of Computer Science Mohammad Ali Jinnah University Karachi Pakistan Department of Computer Engineering Faculty of Engineering and Architecture Beykoz University Istanbul Türkiye Faculty of Information Sciences and Humanities NED University of Engineering and Technology Karachi Pakistan

The term Internet of Things (IoT) is used to refer as embedded devices or objects with internet access, allowing them to communicate globally, interacting with people and networks. IoT security issues are directly related to the widespread use of its systems. This paper examines the security technologies employed in IoT and their respective features and applications. Additionally, it addresses the various security concerns associated with IoT-based systems and services, highlighting their occurrence within the three-layer framework of IoT. These days, as IoT introduces advanced application architectures that minimizes human intervention by integrating businesses in some way to address digital computer processes by communicating within a given network. Moreover, it will be discussed the key technologies associated with Advanced Encryption Standards (AES) and the changes brought in market value and usage of services that has been taken place b since the IoT incorporated.

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The Role of AI in Modern Classroom Management

The Role of AI in Modern Classroom Management

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Unnati Gayaki Subodh Daronde Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Artificial Intelligence is changing the way classrooms are managed, encouraging new ideas, making things easier, and helping students learn in their own way. Adaptive platforms, smart tutoring systems, and tools like I-MINDS customize learning experiences to fit different needs while making it easier for teachers. It takes care of jobs like grading, tracking attendance, and analyzing behavior, allowing teachers to concentrate on important thinking skills and emotional growth. AI also enhances teamwork through group tools, promotes fairness, and assists in real-time decision-making. Technologies like VR, AR, and MR allow people to learn through experience, especially with hard subjects. Incorporating AI learning into school curricula readies students for a future driven by AI. Despite issues with data privacy, biased algorithms, and ethical questions, AI is making learning environments inclusive, dynamic, and just. New technology, combined with ways that are focused on people, is how AI seeks to transform education into a holistic and flexible system for learners and teachers alike.

关键词： Training Heuristic algorithms Learning (artificial intelligence) Transforms Real-time systems Teamwork Planning Performance analysis Problem-solving Artificial intelligence

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