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SSRN 2024年

作者： Dhiman, Gaurav Yasmeen, Safia Kaur, Amandeep Kaur Singh, Dalwinder Devi, Ritika Kaur, Ravinder Kumar, Rakesh Department of Mathematics Lovely Professional University Punjab Phagwara144411 India College of Computer Science and Engineering University of Ha’il Kingdom of Ha’il Saudi Arabia Department of Computer Science Government Bikram College of Commerce Patiala147001 India University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Mohali Gharuan140413 India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun248002 India Division of Research and Development Lovely Professional University Phagwara144402 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140601 India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Computer Science and Technology Lovely Professional University Punjab Phagwara144411 India

This paper proposes a composite strategy for solving linear fractional programming problems in an imprecise environment. To manage the uncertainties and imprecisions associated with real-world decision-making situations, the strategy efficiently combines the benefits of both fuzzy programming and linear fractional programming techniques. The suggested method uses a fuzzy programming methodology to fuzzify the right-hand value of the constraints, then uses a linear fractional programming strategy to obtain the best outcome. Defuzzification is utilized for converting the fuzzy dilemma into a precise problem, which is then solved using linear programming strategies. The effectiveness of the suggested strategy in resolving complicated decision-making issues in fuzzy environments is shown through a case study. The results indicate that the composite approach can provide a robust and efficient solution to a variety of real-world problems with decision-making. © 2024, The Authors. All rights reserved.

关键词： Decision making

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space

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Procedia computer science 2023年 227卷 1046-1053页

作者： Fergianto E. Gunawan Arief S. Budiman Bens Pardamean Endang Juana Sugiarto Romeli Tjeng W. Cenggoro Kartika Purwandari Alam A. Hidayat Anak. A.N.P. Redi Muhammad Asrol Industrial Engineering Department BINUS Graduate Program – Master of Industrial Engineering Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia PT Impack Pratama Industri Jakarta 14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Importance of atmospheric gas selection in metal additive manufacturing: Effects on spatter, microstructure, and mechanical properties

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Keikinzoku/Journal of Japan Institute of Light Metals 2022年第5期72卷 220-226页

作者： Amano, Hiroki Ishimoto, Takuya Nakano, Takayoshi Graduate School of Engineering Osaka University 2-1 Yamadaoka Osaka Suita-shi565-0871 Japan TAIYO NIPPON SANSO Corporation Shinagawa-ku Tokyo Japan Division of Materials and Manufacturing Science Graduate School of Engineering Osaka University Osaka Suita-shi Japan Center for Aluminum and advanced Materials Research and International Collaboration School of Sustainable Design Toyama University Toyama Toyama-shi Japan Anisotropic Design and Additive Manufacturing Research Center Osaka University Osaka Suita-shi Japan

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KDAIDL: Kidney Disease Detection Using Artificial Intelligence and Deep Learning

KDAIDL: Kidney Disease Detection Using Artificial Intelligen...

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International Conference on computer and Information Technology (ICCIT)

作者： Mohammad Mazedul Huq Talukdar Mohammad Alamgir Hossain Mohammed Hassan Osman Yagoub Abbker Adam Abdelnasser Abdelwakil Metwally Yasir Ahmad Md Masnoon Kabir Department of Foreign Languages College of Arts and Humanities Jazan University Jazan Saudi Arabia Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Infectious Disease Division ICDDRB International Center for Diarrhoeal Disease Research Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

A remarkable increase in chronic kidney disease (CKD) is in public health nowadays. Timely diagnosis is crucial for the proper treatment of this illness. This work introduces a deep learning model capable of properly predicting the early stages of the disease. The evaluated models are: Deep Belief Network (DBN), Gated Recurrent Unit (GRU), and the proposed innovative method, CNN+DBN model. Our study compares three models under two conditions: without hyperparameter tuning and with hyperparameter adjustment. After doing hyperparameter tuning on a chronic kidney disease dataset, the model attained an accuracy of 99.2%. This accuracy is significantly higher accuracy as compared to other researchers. The DBN model demonstrates superior performance compared to other models regarding accuracy, recall, F1 score, AUC, and precision. This study introduces a CNN-based model that integrates the deep learning capabilities of the DBN with the feature extraction strengths of the CNN. It effectively captures spatial dependencies in the collected data. The extracted features enable the model to process and predict the early stages of kidney disease. The combination of the CNN and DBN models demonstrated high accuracy. Hyperparameter tuning improves accuracy to 98.15%, exceeding the performance of GRU+DBN models. The CNN+DBN model processes this data further. The hyperparameter tuning and the combined model attained an accuracy of 98.15%, surpassing the performance of the GRU+DBN models. The interpretability and performance of deep learning models are critical factors in the development of efficient models. The inherent nature of the system allows healthcare professionals to rely on its predictions. Additionally, hyperparameter tuning enhances the performance of the model. The robustness of the applied model is evident.

关键词： Deep learning Accuracy Computational modeling Salinity (geophysical) Predictive models Feature extraction Chronic kidney disease Robustness Tuning Diseases

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Exploring the Diagnostic Potential of LLMs in Schizophrenia Detection through EEG Analysis

Exploring the Diagnostic Potential of LLMs in Schizophrenia ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Michele Guerra Roberto Milanese Michele Deodato Madalina G. Ciobanu Fausto Fasano Mosaic Research Center – DiBT University of Molise Pesche Italy Department of Control and Computer Engineering Politecnico di Torino Torino Italy Division of Science New York University Abu Dhabi United Arab Emirates Department of Computer Science University of Salerno Fisciano(SA) Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Schizophrenia is a psychiatric disorder that presents significant diagnostic challenges due to its complex neurophysiological characteristics. This paper investigates the potential of Large Language Models (LLMs), such as OpenAI’s GPT-4 and GPT-o1, in detecting schizophrenia through electroencephalography (EEG) analysis. Using the LMSU public ScZ EEG dataset, we conducted a series of experiments involving different types of input data, including raw EEG signals, frequency band summaries, and graphical representations of brain activity. Our findings demonstrate that LLMs can accurately classify schizophrenic and healthy individuals while offering interpretable, clinically relevant insights aligned with established EEG markers. By integrating these models into the diagnostic workflow, we explore the concept of Symbiotic AI, where LLMs act as cognitive collaborators, enhancing clinicians’ ability to analyze complex data efficiently and transparently. This approach not only improves diagnostic accuracy but also facilitates real-time decision-making, paving the way for earlier and more precise detection of schizophrenia in clinical settings.

关键词： Symbiosis Training Analytical models Accuracy Decision making Schizophrenia Brain modeling Electroencephalography Real-time systems Data models

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Cryo-EM structures of human m6A writer complexes

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细胞研究（英文版） 2022年第11期32卷 982-994页

作者： Shichen Su Shanshan Li Ting Deng Minsong Gao Yue Yin Baixing Wu Chao Peng Jianzhao Liu Jinbiao Ma Kaiming Zhang State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and DevelopmentMultiscale Research Institute of Complex SystemsDepartment of Biochemistry and BiophysicsSchool of Life SciencesFudan UniversityShanghaiChina MOE Key Laboratory for Cellular Dynamics and Division of Life Sciences and Medicine University of Science and Technology of ChinaHefeiAnhuiChina MOE Key Laboratory of Macromolecular Synthesis and Functionalization Department of Polymer Science and EngineeringZhejiang UniversityHangzhouZhejiangChina National Facility for Protein Science in Shanghai Zhangjiang LaboratoryShanghai Advanced Research InstituteChinese Academy of ScienceShanghaiChina State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and DevelopmentMultiscale Research Institute of Complex SystemsDepartment of Biochemistry and BiophysicsSchool of Life SciencesFudan UniversityShanghaiChina Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Guangdong-Hong Kong Joint Laboratory for RNA MedicineRNA Biomedical InstituteMedical Research CenterSun Yat-Sen Memorial HospitalSun Yat-Sen UniversityGuangzhouGuangdongChina

N6-methyladenosine(m6A)is the most abundant ribonucleotide modification among eukaryotic messenger *** m6A"writer"consists of the catalytic subunit m6A-METTL complex(MAC)and the regulatory subunit m6A-METTL-associated complex(MACOM),the latter being essential for enzymatic ***,we report the cryo-electron microscopy(cryo-EM)structures of MACOM at a 3.0-A resolution,uncovering that WTAP and VIRMA form the core structure of MACOM and that ZC3H13 stretches the conformation by binding ***,the 4.4-? resolution cryo-EM map of the MACOM-MAC complex,combined with crosslinking mass spectrometry and GST pull-down analysis,elucidates a plausible model of the m6A writer complex,in which MACOM binds to MAC mainly through WTAP and METTL3 *** combination with in vitro RNA substrate binding and m6A methyltransferase activity assays,our results illustrate the molecular basis of how MACOM assembles and interacts with MAC to form an active m6A writer complex.

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Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies

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Frontiers of Environmental science & engineering 2022年第5期16卷 115-125页

作者： Bingdi Liu Lin Zhang Jason H.Knouft Fangqiong Ling Department of Energy Environmental and Chemical EngineeringWashington University in St.LouisSt.LouisMO 63130USA Department of Biology Saint Louis UniversitySt.LouisMO 63103USA National Great Rivers Research and Education Center East AltonMO 62035USA Department of Computer Science and Engineering Washington University in St.LouisSt.LouisMO 63130USA Division of Biological and Biomedical Sciences Washington University in St.LouisSt.LouisMO 63110USA Division of Computational and Data Science Washington University in St.LouisSt.LouisMO 63130USA

Microbial communities inhabiting river ecosystems play crucial roles in global biogeochemical cycling and pollution *** variations in local microbial assemblages are important for detailed understanding of community assembly and developing robust biodiversity sampling ***,we intensely analyzed twenty water samples collected from a one-meter spaced transect from the near-shore to the near-center in the Meramec River in eastern Missouri,USA and examined the microbial community composition with 16S rRNA gene amplicon *** microbiomes across the transect exhibited extremely high similarity,with Pearson’s correlation coefficients above 0.9 for all pairwise community composition ***,despite the high similarity,PERMANOVA revealed significant spatial differences between near-shore and nearcenter communities(p=0.001).Sloan’s neutral model simulations revealed that within-transect community composition variation was largely explained by demographic stochasticity(R^(2)=0.89).Despite being primarily explained by neutral processes,LefSe analyses also revealed taxa from ten families of which relative abundances differed directionally from the bank to the river center,indicating an additional role of environmental ***,the local variations within a river transect can have profound impacts on the documentation of alpha ***-accumulation curves indicated that even twenty samples did not fully saturate the sampling effort at the genus level,yet four,six and seven samples were able to capture 80%of the phylum-level,family-level,and genus-level diversity,*** study for the first time reveals hyperlocal variations in riverine microbiomes and their assembly mechanisms,demanding attention to more robust sampling strategies for documenting microbial diversity in riverine systems.

关键词： Microbiome Freshwater Taxon accumulation curve Community assembly

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Zn-doped nickel iron(oxy)hydroxide nanocubes passivated by polyanions with high catalytic activity and corrosion resistance for seawater oxidation

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Journal of Energy Chemistry 2023年第6期81卷 82-92,I0004页

作者： So Jung Kim Heechae Choi Jeong Ho Ryu Kang Min Kim Sungwook Mhin Arpan Kumar Nayak Junghwan Bang Minyeong Je Ghulam Ali Kyung Yoon Chung Kyeong-Han Na Won-Youl Choi Sunghwan Yeo Jin Uk Jang HyukSu Han Department of Energy Engineering Konkuk University120 Neungdong-roGwangjin-guSeoul 05029Republic of Korea Theoretical Materials&Chemistry Group Institute of Inorganic ChemistryUniversity of CologneGreinst.650939CologneGermany Materials Lab Geobuk-ro 25-50Incheon 22793Republic of Korea Department of Materials Science and Engineering Korea National University of Transportation50 Daehak-roChungjuChungbuk 27469Republic of Korea Korea Institute of Industrial Technology 137-41 Gwahakdanji-roGangneung-siGangwon 25440Republic of Korea Department of Advanced Materials Engineering Kyonggi UniversitySuwon 16227Republic of Korea Korea Institute of Industrial Technology 156 Gaetbeol-roYeonsu-guIncheon 406-840Republic of Korea U.S.-Pakistan Center for Advanced Studies in Energy(USPCASE) National University of Sciences and Technology(NUST)H-12IslamabadPakistan Energy Storage Research Center Korea Institute of Science and TechnologyHwarang-ro 14-gil 5Seongbuk-guSeoul 02792Republic of Korea Division of Energy and Environment Technology KIST SchoolKorea University of Science and TechnologySeoul 02792Republic of Korea Department of Metal and Materials Engineering Gangneung-Wonju National University 7 JukheongilGangneungGangwon 25457Republic of Korea Smart Hydrogen Energy Center Gangneung-Wonju National University7 JukheongilGangneungGangwon 25457Republic of Korea Innovative Fuel Development Division Korea Atomic Energy Research InstituteDaedeok-daero 989-111Yuseong-guDaejeon 305-353Republic of Korea

Electrochemical water splitting to produce hydrogen fuel is a promising renewable energy-conversion ***-scale electrolysis of freshwater may deplete water resources and cause water scarcity ***,seawater electrolysis is a potential solution to the future energy and water *** seawater electrolysis,it is critical to develop cost-effective electrocatalysts to split seawater without chloride ***,we present zinc-doped nickel iron(oxy)hydroxide nanocubes passivated by negatively charged polyanions(NFZ-PBA-S)that exhibits outstanding catalytic activity,stability,and selectivity for seawater *** dopants and polyanion-rich passivated surface layers in NFZ-PBA-S could effectively repel chlorine ions and enhance corrosion resistance,enabling its excellent catalytic activity and stability for seawater oxidation.

关键词： Seawater splitting Oxygen evolution reaction Electrocatalyst Layered double hydroxide Sulfidation

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OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation

OAFormer: Learning Occlusion Distinguishable Feature for Amo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhixuan Li Ruohua Shi Tiejun Huang Tingting Jiang Advanced Institute of Information Technology Peking University Hangzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

The Amodal Instance Segmentation (AIS) task aims to infer the complete mask of occluded instance. Under many circumstances, existing methods treat occluded objects as unoccluded ones, and vice versa, leading to inaccurate predictions. This is because existing AIS methods do not explicitly utilize the occlusion rates of each object as supervision. However, occlusion information is critical for the methods to recognize whether the target objects are occluded. Hence we believe it is vital for the method to be distinguishable about the degree of occlusion for each instance. In this paper, a simple yet effective Occlusion-aware transformer-based model, OAFormer, is proposed for accurate amodal instance segmentation. The goal of OAFormer is to learn the occlusion discriminative features. Novel components are proposed to enable OAFormer to be occlusion distinguishable. We conduct extensive experiments on two challenging AIS datasets to evaluate the effectiveness of our method. OAFormer outperforms state-of-the-art methods by large margins.

关键词： Target recognition Signal processing Transformers Acoustics Artificial intelligence Task analysis Speech processing

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A Robust Slot Filling Model based on LSTM and CRF for IoT Voice Interaction

A Robust Slot Filling Model based on LSTM and CRF for IoT Vo...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Jbene, Mourad Tigani, Smail Saadane, Rachid Chehri, Abdellah Laboratory Engineering system SIRC/LAGeS-EHTP Hassania School of Public Works Casablanca Morocco Euro-Med University Fes 51 Engineering Unit Euro-Med Research Center Morocco Royal Military College of Canada Department of Mathematics and Computer Science Kingston Canada

ISBN: (纸本)9781665459754

The Internet Of Things (IoT) is an emerging paradigm representing a network of infrastructure connecting different types of devices in different places. Recently, IoT-based Conversational systems gained more attention, providing users with a new human-computer interaction mode. These new systems allow users to interact with the IoT ecosystem through a virtual assistant. They also heavily rely on Natural Language Processing (NLP) techniques for intelligent and efficient communication. Understanding the user's needs is the first step toward a more humanized IoT interaction system. This can be achieved using Natural language understanding (NLU) modules. Slot Filling is one of the core sub-tasks in NLU. It is an active research area focusing on extracting attribute values from the user's utterance. In this study, we propose an LSTM-based model for slot filling. The model benefits from contextual embeddings extracted from the BERT transformer model, the attention mechanism, and the conditional random field (CRF) that is known to model strong dependence among adjacent tags in the output sequence. Experiments show that our model achieved competitive results on two widely used benchmark datasets. © 2022 IEEE.

关键词： Internet of things

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