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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

computer systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National science Review 2025年第5期12卷 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

关键词： pretrained model diabetes continuous glucose monitoring glucose dynamics

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Analysis the Research Hotspots and Key Technical of Intelligent Manufacturing 22

Analysis the Research Hotspots and Key Technical of Intellig...

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4th International Conference on Management science and Industrial engineering, MSIE 2022

作者： Cai, Yu Wu, Jiahao Zaheer, Muhammad Department of Computer Software Engineering Institute of Guangzhou Guangzhou51098 China College of Computer Information and Engineering Nanchang Institute of Technology Nanchang330044 China Southern University of Science and Technology Shenzhen518000 China

ISBN: (纸本)9781450395816

Intelligent manufacturing is one of the keys to open up the future of life, and multidisciplinary interactions have shown its strong vitality. This study conducts a scientometric review of the global research published since 1987 on intelligent manufacturing, through citation, keyword, clustering and keywords burst analysis. A total of 3994 research documents are taken as data sources, which are retrieved from the Web of science Core Collection database from 1987 to 2021. Using the CiteSpace analysis tool, this paper discussed the development process of the relevant years of intelligent manufacturing, analyzed the research hotspots and their technology, presented the emerging models and techniques, and identified the latest research evolution and trends. The results will provide reference for scientific scholars in future research in the field of intelligent manufacturing. © 2022 ACM.

关键词： Industrial research

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Optimizing Existing SHM systems: Retasking as a Self-Healing Solution for Improved Fault Tolerance

Optimizing Existing SHM Systems: Retasking as a Self-Healing...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Aaron Mano Cherian D. Ajitha Avanish Gouraha Dipanshu Mandal School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamilnadu India Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamilnadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This paper focuses on self-healing algorithms in structural health monitoring (SHM) systems centered around the enhancement of resilience and adaptability of the systems. In this study, imports from existing methods (clustering, Fault Tolerant Multiple Redundancy (FTMR) and reinforcement learning) are analyzed against the choice of creating a novel retasking algorithm designed for dynamic resource redistribution and optimal monitoring coverage. Unlike conventional methods, retasking will allow adapting the coverage in real time, whereby system down time will be reduced, with less computational load achieved through task redistribution through functional sensors. Findings showed that retasking improved reliability and scalability of the SHM systems drastically, providing a simple yet powerful resolution towards modern infrastructure monitoring. This study stresses the retasking capability to redefine self-healing in the SHM systems for future directions in infrastructure safety.

关键词： Training Heuristic algorithms Fault tolerant systems Redundancy Clustering algorithms Reinforcement learning Solids Real-time systems Monitoring Stress

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Development and Analysis of a Versatile Dataset of Speech, Real and Synthesized, of Arabic Learners 3

Development and Analysis of a Versatile Dataset of Speech, R...

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3rd International Conference on Computing and information Technology, ICCIT 2023

作者： Alsulaiman, Mansour Algabri, Mohammed Mathkour, Hassan Bencherif, Mohamed A. Muhammad, Ghulam Al-Gahtani, Saad Faisal, Mohammed Mekhtiche, Mohamed Amine Alfakih, Taha King Saud University College of Computer and Information Sciences Computer Engineering Department Riyadh Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia King Saud University College of Computer and Information Sciences Computer Science Department Riyadh Saudi Arabia Riyadh Saudi Arabia King Saud University Arabic Language Institute Saudi Arabia Center of Ai & Robotics Kuwait

ISBN: (纸本)9798350321487

computer-Aided Pronunciation Training (CAPT) systems are gaining popularity recently due to the advancements in deep neural networks (DNN) and machine learning and the availability of databases of speech of language learners. Unfortunately, research in Arabic CAPT systems suffer from the lack of CAPT datasets compared to other languages. In this paper, we present the details of and the ideas used in the development of a versatile dataset of speech, real and synthesized, of Arabic Learners. To develop the dataset, we utilized an existing Arabic speech corpus, King Saud University Speech Database (KSU-SD). KSU-SD's main application was a speaker recognition system, but it was designed to be useful in other applications such as CAPT systems. KSU-SD includes a large number of speakers from diverse nationalities (Saudis, Arabs, and Non-Arabs). KSU-SD contains the recording of about 60 non-native speakers from more than 20 nationalities, hence we selected it to build the non-native Arabic-CAPT corpus. The developed corpus consists of transcribed, segmented, and annotated speech, which makes it suitable for building Arabic CAPT systems. In addition to presenting the details of developing the dataset, we also present an analysis of the text and speech errors of the dataset. The dataset was verified by many CAPT systems. © 2023 IEEE.

关键词： Deep neural networks

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Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

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Intelligent Automation & Soft Computing 2023年第9期37卷 3101-3124页

作者： Waqas Ahmad Hikmat Ullah Khan Fawaz Khaled Alarfaj Saqib Iqbal Abdullah Mohammad Alomair Naif Almusallam Department of Computer Science COMSATS University IslamabadWah CampusWah47040Pakistan Department of Computer Science Namal UniversityMianwali42201Pakistan Department of Management Information Systems(MIS) College of Business AdministrationKing Faisal University(KFU)Al-Ahsa31982Saudi Arabia Department of Software Engineering Al Ain UniversityAl Ain64141United Arab Emirates

Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding *** this regard,prior methodologies widely utilize either word embedding or tree-based ***,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information ***,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a ***,the tree-based structure conserves the grammatical and logical dependencies of *** addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted ***,knowledge of the position-oriented information of words in a sentence has been considered *** this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or *** the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification *** this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position *** three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their *** addition,these terms have been further filtered with the attention mechanism,which determines the most significant *** empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard *** experimental resu

关键词： Sentiment analysis word embedding aspect extraction consistency tree multichannel convolutional neural network contextual position information

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Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

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computer systems science & engineering 2023年第8期46卷 1901-1916页

作者： Mohammad Yamin Sarah Basahel Saleh Bajaba Mona Abusurrah ELaxmi Lydia Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Business Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

Recently,there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy(DR).DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed *** detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus *** article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy(DLLSHDM-DR)on Retinal Fundus *** proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making *** the DLLSHDM-DR technique,image preprocessing is initially performed to improve the quality of the fundus ***,the DLLSHDM-DR applies HybridNet for producing a collection of feature *** retinal image classification,the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer(EPO)with a Deep Recurrent Neural Network(DRNN).The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our *** assuring the improved performance of the DLLSHDMDR model,a wide range of experiments was tested on the EyePACS *** comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.

关键词： Decision making healthcare sector deep learning diabetic retinopathy emperor penguin optimizer

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Intelligent Recognition of Immune Cells: Analysis and Research 19

Intelligent Recognition of Immune Cells: Analysis and Resear...

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19th IEEE International Conference on e-Business engineering, ICEBE 2023

作者： Li, Chih-En Liu, Wei-Lun Lai, Kun-Yi Chiang, Yu-Hsiang Yan, Xiaowei Chen, Hao-Xiang Li, Hsu-Ching Cheng, Wan-Shu National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung Taiwan Institute for Systems Biology Seattle Washington United States Providence University Dep. of Computer Science and Information Management Taichung Taiwan

ISBN: (纸本)9798350325553

Immunotherapy utilizes the potential of the patient's immune system to fight disease. T-lymphocytes have become the central focus of the immune system in the fight against cancer. One of the fundamental studies in immunotherapy is how to accurately identify various types of immune cells. Therefore, the study of immune-related cells will be an important reference for the treatment of COVID19 or other related diseases. In this paper, a series of experiments were conducted with COVID-19 patients as the starting point to design a strategy to identify the important subtypes of CD$4^{+}$T cells. In this paper, an intelligent machine learning algorithm is proposed according to the results of different experiments of linear and nonlinear dimension reduction algorithms, integrating the important characteristics of these two algorithms and improving the performance. Our experimental results show that the algorithm designed based on the front-end experiments can effectively distinguish the important lymphocytes. The proposed algorithm not only improves the performance, but also the dimension reduction procedure keeps the important features of both global and regional data. The experimental results of this paper will be beneficial for the subsequent analysis of the clustering in identifying important subtypes of CD$4^{+}$T cells. Most importantly, this wm significantly improve the procedure of manually integrating and labeling the recorded data after several tedious and monotonous analyses with external software. © 2023 IEEE.

关键词： COVID-19

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Development of Low Resource Machine Learning Models for Child Cognitive Ability Assessments 4

Development of Low Resource Machine Learning Models for Chil...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Kahawanugoda, Adithya Gnanarathna, Kulanika Meegoda, Nilan Monarawila, Randika Samarasinghe, Pradeepa Lindamulage, Asiri Gawesha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

Automated cognitive assessment tools are state-of-the-art in assessing cognition development. Due to the low availability of resources, building automated cognitive ability evaluation tools is challenging. This study focuses on developing machine learning models using a limited amount of data to assess Reasoning IQ, Knowledge IQ, Mental Chronometry and Attention-levels of Sinhala-speaking children between the age of 7 to 9 years. Our solution includes Sinhala speech recognition systems, image classification models, gaze estimation, blink count detection and facial expression recognition models to evaluate the above four cognitive ability measuring factors. Open domain speech recognition has been used to evaluate complex Sinhala child verbal responses and limited vocabulary responses were assessed using an end-to-end speech recognition system, respectively achieving 40.1% WER and 97.14% accuracy. Additionally, the image classification models for handwritten Sinhala letter recognition and two shape recognition models have gained 97%, 89% and 99% accuracy. The linear regression model for attention level evaluation that utilizes the inputs from a combination of eye-gaze estimation, facial expression recognition and blink rate detection models has gained 85% accuracy. © 2022 IEEE.

关键词： Image classification

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Voice Enabled Intelligent Programming Assistant 4

Voice Enabled Intelligent Programming Assistant

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Wataketiya, Ravindu Chandrasiri, Navinda Kithsiri, Ramesh Malwatta, Hirush Nadeeshani, Madhuka Siriwardana, Samanthi Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

In modern era where software development is of vital importance, software developers are challenged with conditions like Repetitive Strain Injury (RSI) which hinders their ability to work effectively. Furthermore, people with difficulties with using their hands also find it challenging to program in the traditional manner. As a solution, coding with one's voice has been experimented with, but current solutions lack interactivity and are harder to use and setup leaving much room for improvement in this domain. In this research work, by using input classifier models with accuracies over 90%, intent classifiers with accuracies over 70%, code parsing and various human computer interaction techniques, we developed a conversationally interactive, programming language agnostic, easy to setup and easy to use Voice Coding Assistant. This will potentially help a global audience of programmers to achieve their goals and improve productivity and lead a healthier life. We have named the system thus developed, "Venic". © 2022 IEEE.

关键词： Human computer interaction

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