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Integration of data science with the intelligent IoT(IIoT):Current challenges and future perspectives

Digital Communications and Networks 2025年第2期11卷 280-298页

作者： Inam Ullah Deepak Adhikari Xin Su Francesco Palmieri Celimuge Wu Chang Choi Department of Computer Engineering Gachon UniversitySeongnamSujeong-gu 13120Republic of Korea University of Electronic Science and Technology of China Chengdu 611731China College of Information Science and Engineering Hohai University213000China Department of Computer Science University of SalernoItaly Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1ChofugaokaChofu-shiTokyo182-8585Japan

The Intelligent Internet of Things(IIoT)involves real-world things that communicate or interact with each other through networking technologies by collecting data from these“things”and using intelligent approaches,such as Artificial Intelligence(AI)and machine learning,to make accurate *** science is the science of dealing with data and its relationships through intelligent *** state-of-the-art research focuses independently on either data science or IIoT,rather than exploring their ***,to address the gap,this article provides a comprehensive survey on the advances and integration of data science with the Intelligent IoT(IIoT)system by classifying the existing IoT-based data science techniques and presenting a summary of various *** paper analyzes the data science or big data security and privacy features,including network architecture,data protection,and continuous monitoring of data,which face challenges in various IoT-based *** insights into IoT data security,privacy,and challenges are visualized in the context of data science for *** addition,this study reveals the current opportunities to enhance data science and IoT market *** current gap and challenges faced in the integration of data science and IoT are comprehensively presented,followed by the future outlook and possible solutions.

关键词： Data science Internet of things(IoT) Big data Communication systems Networks Security Data science analytics

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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Comparative Analysis of Edge Detection Algorithms with Particle Swarm Optimization for Enhanced Image Processing 11

Comparative Analysis of Edge Detection Algorithms with Parti...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Rahayu, Sri Nurtantio Andono, Pulung Pujiono Zainul Fanani, Ahmad Dian Nuswantoro University Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia Institut Teknologi Garut Computer Science Department Garut Indonesia

ISBN: (纸本)9798350389654

Edge detection plays an important role in various fields by identifying object boundaries and supporting advanced image analysis, such as segmentation, recognition, and tracking. Many edge detection algorithms, such as Canny, Laplacian, and Prewitt, have been developed to address issues such as noise sensitivity, computational complexity, and detection accuracy. This research compares the optimized algorithms using Particle Swarm Optimization (PSO). The results of this study show that the optimized algorithm provides better performance based on the evaluation conducted using entropy, Mean Squared Error (MSE), and computation time, and has practical implications. This research also determines the most effective edge detection technique in various image processing scenarios, thus contributing to the optimization of image analysis workflows in real-world contexts. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN INDONESIAN PRISONS 22

EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN IN...

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22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Imandeka, Ejo Hidayanto, Achmad Nizar Suhartanto, Heru Pidanic, Jan Department of Computer Science Faculty of Computer Science University of Indonesia Depok Indonesia Department of Electrical Engineering Faculty of Electrical Engineering and Informatics Pardubice University Pardubice Czech Republic

The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as limited resources, social stigma, isolation, communication barriers, and security concerns. Additionally, there's a scarcity of resources dedicated to promoting knowledge sharing in secure environments. This study aims to dig deep into the key factors that influence how knowledge is shared in Indonesian prisons. To navigate this intricate terrain, the study uses questionnaires distributed among Prison officers to understand the complex relationship between the identified critical factors and knowledge sharing in Prison. Leadership support, organizational culture, organizational structure, incentives, and individual beliefs emerge as pivotal elements in knowledge sharing. It's also noted that the connection between these critical factors and the intention to share knowledge is significantly influenced by organizational structure. Recognizing and addressing critical factors in knowledge sharing within prisons not only improves rehabilitation within prison walls but also transforms the broader criminal justice system. This approach fosters evidence-based practices, better communication, and a comprehensive understanding of factors leading to successful rehabilitation and reduced recidivism, ultimately creating a more just and effective system. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Critical Factors Indonesia Knowledge Knowledge Sharing Prison

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Approximation Polynomial-Time Algorithms for Consistency-Aware Multi-Server Network Design in Delay-Sensitive Applications

IEEE Networking Letters

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IEEE Networking Letters 2025年第2期7卷 135-139页

作者： Oda, Masaki Kawabata, Akio Oki, Eiji Kyoto University Graduate School of Informatics Kyoto606-8501 Japan Toyohashi University of Technology Department of Computer Science and Engineering Aichi 441-8580 Japan

This paper proposes two polynomial-time approximation algorithms for allocating servers to design a consistency-aware multi-server network for delay-sensitive applications. Each algorithm selects servers and determines the main-secondary server pairs to minimize the total delay. Previous work has not provided any polynomial-time algorithm. The proposed algorithms are theoretically guaranteed to output an approximate value within three times the optimal value. Numerical results show that the more computationally efficient of the two algorithms is 46.4 to 5.26 × 104 times faster than an integer linear programming technique, while the maximum delay is, on average, merely 1.0196 times the optimal value. © 2019 IEEE.

关键词： Integer linear programming

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Predictive Analysis of Sarajevo's AQI using Machine Learning Models for Varied Data Granularity and Prediction Windows 47

Predictive Analysis of Sarajevo's AQI using Machine Learning...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Zolota, Emina Hasić, Vahidin Mević, Amina Delić, Amra Krivić, Senka University Of Sarajevo Faculty Of Electrical Engineering Department For Computer Science And Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350382495

This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM) - to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model's efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments. © 2024 IEEE.

关键词： Air quality

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Fine-Tuning GPT-4o-Mini for Programming Questions Generation

Fine-Tuning GPT-4o-Mini for Programming Questions Generation

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2024 International Conference on computer and Applications, ICCA 2024

作者： Hesham, Alaa Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt

ISBN: (纸本)9798350367560

Creating programming questions that are both meaningful and educationally relevant is a critical task in computer science education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generate meaningful questions by leveraging semantic feature extraction and well- crafted prompts. The approach addresses the limitations of traditional generative models, offering a deeper understanding of programming code and producing questions that are precise, diverse, and relevant to a given code snippets. The proposed framework incorporates essential code elements, such as control structures and method attributes, to generate questions that align with programming concepts. Evaluation metrics used were BLEU, ROUGE-1, and ROUGE-L to evaluate the model's performance. The findings reveal that the model achieves better structural coherence and conceptual relevance while focusing on contextual understanding over exact term matching. This work highlights the potential of the proposed approach to advance teaching and assessment methods in computer science. © 2024 IEEE.

关键词： Program processors

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DeBERTa-GRU: Sentiment Analysis for Large Language Model

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computers, Materials & Continua 2024年第6期79卷 4219-4236页

作者： Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah Department of Informatics for Business College of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Department of Computer Science and Technology Beijing University of Chemical TechnologyBeijing100029China Department of Computer Science Government College University FaisalabadFaisalabadPunjab38000Pakistan

Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.

关键词： DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model

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An Automated Framework to Detect Emotions from Contextual Corpus

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International Journal of Advanced computer science and Applications 2023年第3期14卷 865-874页

作者： Thallapalli, Ravikumar Narsimha, G. Faculty of Informatics-Department of CSE Osmania University Telangana Hyderabad India Department of Computer Science and Engineering JNTUH University College of Engineering Telangana SultanpurSangareddy India

The emotion extraction or opinion mining is one of the key tasks for any text processing frameworks. In recent times, the use of opinion mining has gained a lot of potential due to the application of the potential customized aspects of the consumer relations and other customized applications. However, the application of sentiment analysis or opinion mining is highly challenging as the accuracy of the sentiment analysis depends on the input text corpus. The input text corpus can be highly fluctuating due to the inclusion of emojis or local language influences and finally the use of a wide variety of the regional languages. A good number of parallel research outcomes have aimed to solve these challenges in the recent time. However, most of the parallel research outcomes have primarily three challenges kept unsolved as firstly, the emojis in the text corpus is mainly removed but not translated into sentiment scores, secondly, the translation of the texts from various regional languages and the translation is mainly true translations rather than the contextual translation. Finally, the use of the dictionaries in the actual translation tasks takes a lot of time to process and must be reduced. Henceforth, in order to solve these challenges, this work proposed a framework to automate the weighted emoji-based sentiment analysis, Unicode based translation process to reduce the time complexity and finally use the collaborative sentiment analysis scores to build the final sentiment models. This work results into nearly 97% accuracy and nearly 50% reduction in the time complexity © 2023, International Journal of Advanced computer science and *** Rights Reserved.

关键词： Sentiment analysis

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Deep Learning Quantitative Susceptibility Mapping With Reduced Training Time Based on Local Field Transformation

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年第2期9卷 1527-1538页

作者： Wu, Ming-Long Huang, De-Rong Cheng, Jhih-Shan National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan National Cheng Kung University Institute of Medical Informatics Tainan701 Taiwan

A quantitative susceptibility mapping (QSM) approach using single-orientation imaging data is proposed in this study. The proposed method generates local field maps at five predefined orientations via deep learning from single-orientation local field maps, followed by physical dipole inversion using the analytical Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) algorithm. We termed the method pseudo COSMOS (pCOSMOS). By isolating the physical dipole inversion process from deep learning, training of the neural network model becomes greatly accelerated. Brain imaging data from 10 healthy subjects (7 training and 3 testing) acquired at 3T with five different orientations relative to the main magnetic field were included to evaluate the reconstruction quality. Results compared against two iterative (Morphology Enabled Dipole Inversion or MEDI, and Structural Feature-based Collaborative Reconstruction or SFCR) and two deep learning QSM approaches (QSMnet and Learned Proximal Convolutional Neural Network or LPCNN) using COSMOS as the reference standard revealed that the susceptibility maps obtained from pCOSMOS yielded global quantitative performance comparable to QSMnet and LPCNN, while being prominently superior to MEDI and SFCR. Estimation errors in magnetic susceptibility were between 0.01 and 0.02 ppm voxel-wise, and 0.006 ± 0.004 ppm for five selected gray matter areas. Visual inspection further suggested better preserved tissue boundaries in pCOSMOS than QSMnet and LPCNN. Training time was reduced from 2 days for LPCNN and 12 hours for QSMnet to 3.2 hours for pCOSMOS. It is concluded that the proposed pCOSMOS method is an alternative deep learning QSM approach when computational efficiency is essential. © 2017 IEEE.

关键词： Brain mapping

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