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Computing (ICOCO), IEEE International Conference on

作者： Mahayaudin M. Mansor Nurain Ibrahim Noor Fadhilah Ahmad Radi Nadirah Abdul Rahim Syarul Heiry Yahaya Mohd Aftar Abu Bakar Roslinazairimah Zakaria School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA (Penang Campus) Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.

关键词： Visualization Technological innovation Ethics Emotion recognition Bibliometrics Decision making Focusing Electronic healthcare Object recognition Artificial intelligence

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Challenges of deep learning methods for COVID-19 detection using public datasets

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informatics in Medicine Unlocked 2022年 30卷 100945页

作者： Hasan, Md. Kamrul Alam, Md. Ashraful Dahal, Lavsen Roy, Shidhartho Wahid, Sifat Redwan Elahi, Md. Toufick E. Martí, Robert Khanal, Bishesh Department of Electrical and Electronic Engineering (EEE) Khulna University of Engineering & Technology (KUET) Khulna 9203 Bangladesh Nepal Applied Mathematics and Informatics Institute for Research (NAAMII) Nepal Computer Vision and Robotics Institute University of Girona Spain

Since the COVID-19 pandemic, several research studies have proposed Deep Learning (DL)-based automated COVID-19 detection, reporting high cross-validation accuracy when classifying COVID-19 patients from normal or other common Pneumonia. Although the reported outcomes are very high in most cases, these results were obtained without an independent test set from a separate data source(s). DL models are likely to overfit training data distribution when independent test sets are not utilized or are prone to learn dataset-specific artifacts rather than the actual disease characteristics and underlying pathology. This study aims to assess the promise of such DL methods and datasets by investigating the key challenges and issues by examining the compositions of the available public image datasets and designing different experimental setups. A convolutional neural network-based network, called CVR-Net (COVID-19 Recognition Network), has been proposed for conducting comprehensive experiments to validate our hypothesis. The presented end-to-end CVR-Net is a multi-scale-multi-encoder ensemble model that aggregates the outputs from two different encoders and their different scales to convey the final prediction probability. Three different classification tasks, such as 2-, 3-, 4-classes, are designed where the train–test datasets are from the single, multiple, and independent sources. The obtained binary classification accuracy is 99.8% for a single train–test data source, where the accuracies fall to 98.4% and 88.7% when multiple and independent train–test data sources are utilized. Similar outcomes are noticed in multi-class categorization tasks for single, multiple, and independent data sources, highlighting the challenges in developing DL models with the existing public datasets without an independent test set from a separate dataset. Such a result concludes a requirement for a better-designed dataset for developing DL tools applicable in actual clinical settings. The datas

关键词： Chest computed tomography and X-ray Convolutional neural networks COVID-19 disease Ensemble classifier

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence Big data Data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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Modeling CO2 solubility in polyethylene glycol polymer using data driven methods

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Separation Science and technology (Philadelphia) 2025年第11期60卷 1451-1470页

作者： Lei, YunLi Sead, Fadhel F. Sur, Dharmesh Yadav, Anupam Ballal, Suhas Singh, Abhayveer Krithiga, T. Kumar, Akshay Yuldashev, Farrukh Ahmad, Irfan Sherzod, Samim Guangxi Technological College of Machinery and Electricity Nanning China Department of Dentistry College of Dentistry The Islamic University Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of medical analysis Medical laboratory technique college the Islamic University of Babylon Babylon Iraq Department of Chemical Engineering Faculty of Engineering & Technology Marwadi University Research Center Rajkot India Department of Computer engineering and Application GLA University Mathura India Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India Department of Chemistry Sathyabama Institute of Science and Technology Chennai India Department of Mathematics Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun India Department of Informatics and Its Teaching Methods Tashkent State Pedagogical University Tashkent Uzbekistan Department of Clinical Laboratory Sciences College of Applied Medical Sciences King Khalid University Abha Saudi Arabia Faculty of Engineering Nangarhar University Nangarhar Afghanistan

The solubility of CO2 in polyethylene glycol (PEG) polymer is a critical parameter for optimizing its use in various industrial processes, underscoring the need for precise predictive models. In this research, a Random Forest (RF) machine learning model is meticulously tuned through four sophisticated optimization algorithms: Batch Bayesian Optimization (BBO), Self-Adaptive Differential Evolution (SADE), Bayesian Probability Improvement (BPI), and Gaussian Processes Optimization (GPO). The model leverages a dataset of 164 experimental samples, incorporating essential input parameters such as pressure, PEG molar mass, and temperature to predict CO2 solubility. To prevent overfitting, K-fold cross-validation is applied throughout model training. The efficacy of each optimization method is evaluated using computational runtime and performance metrics, including R-squared (R2), mean squared error (MSE), and average absolute relative error (AARE%). Correlation analysis indicates that pressure has a moderate positive relationship with CO2 solubility (correlation coefficient: 0.58), while PEG molar mass and temperature exhibit weaker associations (0.2 and 0.05, respectively). Among the optimization techniques, RF-BPI proves to be the most effective, delivering superior predictive performance. The results show RF-BPI achieves an R2 of 0.9625 for the training set and 0.9307 for the test set, surpassing other methods. For benchmarking, a conventional multiple linear regression (LR) model was tested, demonstrating significantly lower accuracy (R2 notably inferior to RF-BPI’s 0.9307 on the test set). In terms of computational efficiency, BPI records the shortest runtime (63.9 seconds), while SADE is the least efficient, requiring 2370.2 seconds. Sensitivity analysis further clarifies the relative impact of each input variable on CO2 solubility, affirming the power of data-driven approaches in modeling complex systems. The created models can be seen as promising predictive tools

关键词： Mean square error

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Digital Twins for Dynamic Management of Blockchain Systems

Digital Twins for Dynamic Management of Blockchain Systems

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Simulation Winter Conference

作者： Georgios Diamantopoulos Nikos Tziritas Rami Bahsoon Georgios Theodoropoulos University of Birmingham Edgbaston Birmingham UNITED KINGDOM Southern University of Science and Technology Shenzhen CHINA Department of Informatics and Telecommunications University of Thessaly Lamia GREECE School of Computer Science University of Birmingham Edgbaston Birmingham UNITED KINGDOM Department of Computer Science and Engineering Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen CHINA

ISBN: (纸本)9781665476621

Blockchain systems are challenged by the so-called Trilemma tradeoff: decentralization, scalability, and security. Infrastructure and node configuration, choice of the Consensus Protocol, and complexity of the application transactions are cited among the factors that affect the tradeoff balance. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This paper introduces the utilization of DigitalTwins for this purpose. The novel contribution of the paper is the design of a framework and conceptual architecture of a Digital Twin that can assist in maintaining the Trilemma tradeoffs of time-critical systems. The proposed Digital Twin is illustrated via an innovative approach to the dynamic selection of Consensus Protocols. Simulation results show that the proposed framework can effectively support the dynamic adaptation and management of the Blockchain.

关键词： Analytical models Simulation computer architecture Quality of service Digital twins Consensus protocol Complexity theory

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Transformations in The Ways of Improving from Agriculture 1.0 to 4.0

Transformations in The Ways of Improving from Agriculture 1....

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International Conference on Contemporary Computing and informatics (IC3I)

作者： Shruti Aggarwal Amit Verma Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala Punjab India University Centre for Research and Development Chandigarh University Gharuan Mohali Punjab India

Farming is the need and will consistently be. We human get by on food that is either from plant or from creature. Individuals have developed grounds and breed creatures to acquire nourishment for their endurance since old occasions. This training, known as farming, has advanced after a long haul and reformist cycle, going from Agriculture 1.0 to 4.0. As indicated by the UN Food and Agriculture Organization (FAO) 800 million individuals experiencing hunger and around 8% (650 million) of total populace will be as yet undernourished by 2030. Likewise horticulture portion of worldwide GDP has recently contracted 3% just and we should create 70% more food by 2050. Without a doubt, high requests for food from the overall developing populace are affecting the climate and putting numerous weights on horticultural efficiency. The customary methodology of the horticulture is going through a principal change. Throughout the last numerous long stretches of our set of experiences we have built up a few types of farming, 1.0, 2.0 and now moving towards 3.0 and 4.0 in coming many years. Indeed, today these manifestations are being rehearsed in different places on earth. This paper describes that how the transformation from Agriculture 1.0 to Agriculture 4.0 takes place and the changes in the practices of the agriculture from ancient times to present.

关键词： Training Earth Technological innovation Economic indicators Horticulture Organizations Agriculture

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Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos 38

Retrospective for the Dynamic Sensorium Competition for pred...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Turishcheva, Polina Fahey, Paul G. Vystrčilová, Michaela Hansel, Laura Froebe, Rachel Ponder, Kayla Qiu, Yongrong Willeke, Konstantin F. Bashiri, Mohammad Baikulov, Ruslan Zhu, Yu Ma, Lei Yu, Shan Huang, Tiejun Li, Bryan M. De Wulf, Wolf Kudryashova, Nina Hennig, Matthias H. Rochefort, Nathalie L. Onken, Arno Wang, Eric Ding, Zhiwei Tolias, Andreas S. Sinz, Fabian H. Ecker, Alexander S. Institute of Computer Science and Campus Institute Data Science University of Göttingen Germany Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Ophthalmology Byers Eye Institute Stanford University School of Medicine StanfordCA United States Stanford Bio-X Stanford University StanfordCA United States Wu Tsai Neurosciences Institute Stanford University StanfordCA United States International Max Planck Research School for Intelligent Systems Tübingen Germany Institute for Bioinformatics and Medical Informatics Tübingen University Germany lRomul Russia Institute of Automation Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China The Alan Turing Institute United Kingdom School of Informatics University of Edinburgh United Kingdom Centre for Discovery Brain Sciences University of Edinburgh United Kingdom Simons Initiative for the Developing Brain University of Edinburgh United Kingdom Department of Electrical Engineering Stanford University StanfordCA United States Max Planck Institute for Dynamics and Self-Organization Göttingen Germany

Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive models that connect biological and machine vision. Machine learning has benefited tremendously from benchmarks that compare different models on the same task under standardized conditions. However, there was no standardized benchmark to identify state-of-the-art dynamic models of the mouse visual system. To address this gap, we established the SENSORIUM 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice. This dataset includes responses from 78,853 neurons to 2 hours of dynamic stimuli per neuron, together with behavioral measurements such as running speed, pupil dilation, and eye movements. The competition ranked models in two tracks based on predictive performance for neuronal responses on a held-out test set: one focusing on predicting in-domain natural stimuli and another on out-of-distribution (OOD) stimuli to assess model generalization. As part of the NeurIPS 2023 Competition Track, we received more than 160 model submissions from 22 teams. Several new architectures for predictive models were proposed, and the winning teams improved the previous state-of-the-art model by 50%. Access to the dataset as well as the benchmarking infrastructure will remain online at ***. © 2024 Neural information processing systems foundation. All rights reserved.

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Improving Generalization of Metric Learning via Listwise Self-distillation

arXiv

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arXiv 2022年

作者： Zeng, Zelong Yang, Fan Wang, Zheng Satoh, Shin'ichi The Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan The Digital Content and Media Sciences Research Division National Institute of Informatics Japan The School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University China

Most deep metric learning (DML) methods employ a strategy that forces all positive samples to be close in the embedding space while keeping them away from negative ones. However, such a strategy ignores the internal relationships of positive (negative) samples and often leads to overfitting, especially in the presence of hard samples and mislabeled samples. In this work, we propose a simple yet effective regularization, namely Listwise Self-Distillation (LSD), which progressively distills a model's own knowledge to adaptively assign a more appropriate distance target to each sample pair in a batch. LSD encourages smoother embeddings and information mining within positive (negative) samples as a way to mitigate overfitting and thus improve generalization. Our LSD can be directly integrated into general DML frameworks. Extensive experiments show that LSD consistently boosts the performance of various metric learning methods on multiple datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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GPT-Based Automated Induction: Vulnerability Detection in Medical Software

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Deng, Liangjun Lei, Hang Khan, Fazlullah Srivastava, Gautam Chen, Jingxue Haque, Mainul University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Nottingham Ningbo China School of Computer Science and the Next-Generation Internet of Everything Laboratory Faculty of Science and Engineering Zhejiang Ningbo315104 China Lovely Professional University Division of Research and Development Phagwara India Chandigarh University University Centre of Research and Development Mohali India Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India University of Nottingham Ningbo China Department of Mathematics Faculty of Science and Engineering Zhejiang Ningbo315104 China

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software. © 2025 IEEE. All rights reserved,

关键词： Scalability

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Blockchain-based Secure Outsourcing Data Integrity Auditing for Internet of Things in Cloud-edge Environment

Blockchain-based Secure Outsourcing Data Integrity Auditing ...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yangfei Lin Celimuge Wu Yusheng Ji Jie Li Zhi Liu Graduate School of Informatics and Engineering The University of Electro-Communications Chofu-shi Tokyo Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the feature of limited computing and storage resources of IoT, it is inevitable to store data in the cloud for better services. However, for users to effectively and efficiently inspect those data over the cloud is a critical and open problem. Most public integrity auditing over the cloud schemes requires the user to do a sheer amount of preprocessing work on the local devices, which is unsuitable for IoT devices. With the development of edge computing extending cloud computing, it can provide computing capability for resource-constrained devices in close geographic proximity. In this paper, we design an auditing scheme based on secure computation outsourcing assisted by edge computing, in which the data preprocessing work can be offloaded to the edge server. The experiments show that it reduces the computing load on the devices and improves the efficiency of task processing.

关键词： Cloud computing Data privacy Data integrity Outsourcing Sensors Performance analysis Internet of Things

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