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9th International Conference on information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

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computers, Materials & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Korhan Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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Unseen Named Entity Recognition for Thai Language 21

Unseen Named Entity Recognition for Thai Language

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Kuntichod, Watcharet Lee, Wilaiporn Prayote, Akara King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Computer and Information Science Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9798350377088

Most common challenges in Named-entity recognition (NER) require suitable and annotated datasets, tailored to the specific domain of the documents under analysis, for example, legal, medical, and financial documents each require distinct datasets. Reliable identification of named entities within the training set is crucial. Annotations is a vital role in building an effective mechanism. However, creating training sets demands significant human effort, contributing to the high costs associated with NER. High quality training materials are indispensable for evolving machine-learning algorithms, underscoring the importance of dependable training data. Thus, the primary challenge lies in acquiring and managing data rather than in the algorithms themselves. To overcome most common situations of finding new unseen names, this paper proposes a mechanism to detect a new undiscovered name in Thai language based on sentence contexts. The method uses BI-LSTM model to learn sentence structures and predict a new unseen name from its context. Our framework comprises two main tasks. The first one is word entity extraction. The second task is using the model to identify unrecognized named entities. Evaluation of the proposed method is described with a successful rate of accuracy in discovering newly identified entities. This contributes to the expansion of entity recognition capabilities in Thai language processing. © 2024 IEEE.

关键词： BI-LSTM Herb Entity Unseen Named Entity

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A Novel Inception Deep Learning model for Mobile AI Stroke Prediction system: AI Research and Industry Perspectives for Connected Health 6

A Novel Inception Deep Learning model for Mobile AI Stroke P...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Elbagoury, Bassant M. Maskeliuans, Rytis Zaghow, Marwa Kamel, Nabil Faculty of Computer and Information Sciences Ain Shams University Computer Science and Artificial Intelligence Programs Egypt Faculty of Computer Science and Engineering King Salman International University KSIU Egypt Kaunas University of Technology Faculty of Informatics Department of Multimedia Engineering Lithuania Dell Technologies EMEA Egypt

ISBN: (纸本)9798350373875

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed. © 2024 IEEE.

关键词： Learning systems

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Exponential Synchronization for Mobile Robotic Formation: A Hybrid Dynamic Event-Triggered Intermittent Control Method

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-10页

作者： Chang, Zhenyu Guo, Ying Zong, Guangdeng Qi, Wenhai Zhao, Xudong School of Control Science and Engineering Tiangong University Tianjin China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Engineering Qufu Normal University Rizhao China Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China

To address the formation tracking issue of mobile robotic systems, this paper constructs a novel hybrid dynamic event-triggered intermittent control strategy, which can achieve the exponential synchronization of the systems. Considering the limited control resources, the intermittent control method is introduced into the distributed control strategy to save resources, and the existing intermittent control model is reconstructed to describe the system model better. A hybrid dynamic event-triggered mechanism is developed by combining the dynamic event-triggered method with the time sampling strategy, eliminating the Zeno phenomenon. The control time sequences in the developed intermittent control strategy are automatically selected by the hybrid dynamic event-triggered sequences, rather than artificially designed in advance, which reduces a certain degree of design complexity. The developed control strategy effectively saves control resources while alleviating the burden of network communication. Sufficient conditions for achieving exponential synchronization formation tracking are provided, and the exponential convergence of the formation error is demonstrated through the proposed lemma. Finally, a control task of multi-mobile robots formation is presented to verify the effectiveness of the theoretical analysis. IEEE

关键词： Synchronization

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Towards Holistic Crime Management Through Descriptive Analysis and Predictive Forecasting 6

Towards Holistic Crime Management Through Descriptive Analys...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Wijethunge, Tinaya Edirisinghe, Sampath Jeon, Mansik Kim, Jeehyun Wijesinghe, Ruchire Silva, Bhagya Faculty of Engineering Department of Computer Engineering Nugegoda Sri Lanka Daegu Korea Republic of Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka Malabe Sri Lanka Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

Throughout the evolution of humankind, devising effective crime prevention strategies has been a concern in fostering safer communities. Although achieving a zero-crime rate is not possible yet, using data management methods can contribute to preventing and minimizing crimes. This article presents an integrated approach to proactive crime prevention. Data insights derived from analysis and visualization provide a comprehensive overview of historical trends and scenarios, aiding understanding of past and present situations. The forecasting of crime rates extends this comprehension to the future, spotlighting the severity of the potential threat to the safety of the community. Three distinct prediction models are presented to predict the occurrence of violent/non-violent crimes, the most probable crime type, and the most probable crime location in a spatial-temporal context, facilitating optimal resource allocation of police patrolling. Combining these three analytical components, an application is designed to present this holistic solution for effective crime prevention, with a 73% accuracy in predicting violent crimes. To achieve higher accuracy in predicting the most probable crime types and locations, it is essential to go beyond spatiotemporal context and augment data on other factors that affect the occurrence of crimes. © 2024 IEEE.

关键词： Prediction models

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A Comprehensive Evaluation of State-of-the-Art Deep Learning Models for Road Surface Type Classification

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Intelligent Automation & Soft Computing 2023年第8期37卷 1275-1291页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayao56000Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

In recent years,as intelligent transportation systems(ITS)such as autonomous driving and advanced driver-assistance systems have become more popular,there has been a rise in the need for different sources of traffic situation *** classification of the road surface type,also known as the RST,is among the most essential of these situational data and can be utilized across the entirety of the ITS ***,the benefits of deep learning(DL)approaches for sensor-based RST classification have been demonstrated by automatic feature extraction without manual *** ability to extract important features is vital in making RST classification more *** work investigates the most recent advances in DL algorithms for sensor-based RST classification and explores appropriate feature extraction *** used different convolutional neural networks to understand the functional architecture better;we constructed an enhanced DL model called SE-ResNet,which uses residual connections and squeeze-and-excitation mod-ules to improve the classification *** experiments with a publicly available benchmark dataset,the passive vehicular sensors dataset,have shown that SE-ResNet outperforms other state-of-the-art *** proposed model achieved the highest accuracy of 98.41%and the highest F1-score of 98.19%when classifying surfaces into segments of dirt,cobblestone,or asphalt ***,the proposed model significantly outperforms DL networks(CNN,LSTM,and CNN-LSTM).The proposed RE-ResNet achieved the classification accuracies of asphalt roads at 98.98,cobblestone roads at 97.02,and dirt roads at 99.56%,respectively.

关键词： Road surface type classification deep learning inertial sensor deep pyramidal residual network squeeze-and-excitation module

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Investigations and Time Estimation on Federated Learning for Future Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17387-17398页

作者： Fukumoto, Shun Li, Ruidong Zeng, Kai Nan, Haihan Su, Zhou Kanazawa University Intelligent Computation and Network Laboratory Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan Kunming University of Science and Technology Faculty of Information Engineering Automation Kunming066000 China Kanazawa University Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan George Mason University Electrical and Computer Engineering Department FairfaxVA22030 United States Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work. © 2014 IEEE.

关键词： Stochastic systems

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Dengue Cases Prediction and Analysis using LSTM and Region-specific Data

Dengue Cases Prediction and Analysis using LSTM and Region-s...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ud Deen Shafique, Hafiz Noor Ali, Nauman Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Zunnurain Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan

ISBN: (纸本)9798350373783

Dengue Fever presents a substantial global health challenge, necessitating the development of accurate predictive models for effective disease management. This research paper conducts a comprehensive analysis of a Long ShortTerm Memory (LSTM) neural network-based model for forecasting Dengue cases in the Philippines. The study includes data preprocessing, model development using TensorFlow and Keras, and thorough evaluation using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Rsquared (R2). The dataset, 'Dengue Cases in the Philippines,' provides a detailed historical record of Dengue fever cases and fatalities across various regions, enabling a thorough analysis of temporal and spatial dynamics. Data preprocessing involves label encoding, normalization, and careful data splitting to ensure accurate model training and testing. The LSTM neural network architecture is optimized through hyperparameter tuning, including learning rate, batch size, and epochs, to improve prediction accuracy. Model performance is rigorously evaluated, with an MAE of 913, indicating areas for further improvement. While the model shows promise, there is room for refinement. This study contributes to the field by exploring the feasibility of LSTM models for Dengue prediction, with insights that may inform future research in disease forecasting and public health strategies. Limitations, ethical considerations, and recommendations for further work are discussed. © 2024 IEEE.

关键词： Electronic health record

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