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13th International Conference on ICT Innovations, ICT Innovations 2021

作者： Stefanovska, Elena Gievska, Sonja Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Rugjer Boshkovikj 16 Skopje Macedonia

ISBN: (纸本)9783031042058

Experimentally identifying previously unknown drug-drug interactions (DDIs) that might cause potentially adverse drug reactions or alter drug’s effectiveness when a combination of two or more drugs are used is a costly task. By contrast, many computational-algorithmic approaches have been used as a faster solution to the problem. Our current research efforts have been directed toward comparative performance evaluation of several approaches for discovering and classifying previously unknown interactions between two drugs. In this research, the task of discovering new DDIs have been formalized as a link prediction task in an interaction network constructed on the basis of previously known drug interactions. Several approaches for link prediction have been experimented with to find empirical evidence of their performance standing. Classifying the interaction type of a newly discovered DDI on the basis of the molecular compound of the drugs involved have also been explored. © 2022, Springer Nature Switzerland AG.

关键词： Drug interactions

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Error-Correcting Codes for Combinatorial Composite DNA

Error-Correcting Codes for Combinatorial Composite DNA

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IEEE International Symposium on Information Theory

作者： Omer Sabary Inbal Preuss Ryan Gabrys Zohar Yakhini Leon Anavy Eitan Yaakobi Faculty of Computer Science Technion-Israel Institute of Technology Haifa Israel Faculty of Computer Science Reichman University Hezliya Israel Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Data storage in DNA is developing as a possible solution for archival digital data. Recently, to further increase the potential capacity of DNA-based data storage systems, the combinatorial composite DNA synthesis method was suggested. This approach extends the DNA alphabet by harnessing short DNA fragment reagents, known as shortmers. The shortmers are building blocks of the alphabet symbols, each consisting of a fixed number of shortmers. Thus, when information is read, it is possible that one of the shortmers that forms part of the composition of a symbol is missing and therefore the symbol cannot be determined. In this paper, we model this type of error as a type of asymmetric error and propose code constructions that can correct such errors in this setup. We also provide a lower bound on the redundancy of such error-correcting codes and give an explicit encoder and decoder for our construction. Our suggested error model is also supported by an analysis of data from actual experiments that produced DNA according to the combinatorial scheme. Lastly, we also provide a statistical evaluation of the probability of observing such error events, as a function of read depth.

关键词： Buffer storage Redundancy DNA Symbols Memory Probability Data models

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Malicious Traffic Classification in WSN using Deep Learning Approaches 2

Malicious Traffic Classification in WSN using Deep Learning ...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Gupta, Chhote Lal Prasad Rajassekharan, Dinesh Sharma, Dilip Kumar Elangovan, Mohanraj Myilsamy, Varatharaj Upreti, Kamal Bansal Institute of Engineering and Technology Dept. of Computer Science and Engineering Lucknow India Peninsula College Faculty of Computer Science Peninsula Malaysia Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruchengode637215 India V.S.B College of Engineering Technical Campus Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350369960

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. © 2023 IEEE.

关键词： data routing deep learning LSTM malicious transmission neural network Traffic data transmission WSN

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Sentiment Analysis: Amazon Electronics Reviews Using BERT and Textblob 20

Sentiment Analysis: Amazon Electronics Reviews Using BERT an...

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20th International Conference on Language engineering, ESOLEC 2022

作者： Mahgoub, Abdulrahman Atef, Hesham Nasser, Abdulrahman Yasser, Mohamed Medhat, Walaa M. Darweesh, M. Saeed El-Kafrawy, Passent M. Nile University Egypt Benha University Faculty of Computer and AI Cairo Egypt Nile University Egypt Effat College of Engineering Computer Science Department Saudi Arabia

ISBN: (纸本)9781665453226

The market needs a deeper and more comprehensive grasp of its insight, where the analytics world and methodologies such as 'Sentiment Analysis' come in. These methods can assist people especially 'business owners' in gaining live insights into their businesses and determining wheatear customers are satisfied or not. This paper plans to provide indicators by gathering real world Amazon reviews from Egyptian customers. By applying both Bidirectional Encoder Representations from Transformers 'Bert' and 'Text Blob' sentiment analysis methods. The processes shall determine the overall satisfaction of Egyptian customers in the electronics department-in order to focus on a specific domain. The two methods will be compared for both the Arabic and English languages. The results show that people in *** are mostly satisfied with the percentage of 47%. For the performance, BERT outperformed Textblob indicating that word embedding model BERT is more superior than rule-based model Textblob with a difference of 15%-25%. © 2022 IEEE.

关键词： Sentiment analysis

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Concurrent Fusion LSTM Model for Accurate Prediction of Covid-19 Progression Using Multi-Source Data

Concurrent Fusion LSTM Model for Accurate Prediction of Covi...

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Parthiban M Anna Alphy B Sreedevi Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Ghaziabad Uttar Pradesh India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

It has become a challenge to predict the progress of COVID-19 quickly and well. Such models are good for comparing the actuals versus the predicted cases in an epidemiology study but fail in capturing most of the complex, non-linear relationships in data that end up leading to increased errors in prediction. Concurrent Fusion Long Short-Term Memory (CF-LSTM) model was developed in this study for predicting COVID-19 cases so as to address the aforementioned shortcomings in the currently available models. Multi-source real-time data such as epidemiological records, mobility, and vaccines will be coupled for better prediction. Concurrent Fusion Long Short-Term Memory model process a concurrent fusion framework that provides multifeatured input to realize spatiotemporal dependency and interaction coverage. For the public pandemic's data repository on COVID-19 by Johns Hopkins University, the performance of the models has been experimentally evaluated with the achieve worth of mean absolute error: 0.89 and root mean square error: 1.14 in respect to daily infect rates modeling performance. Scenario simulation shows promising results. It provides insights that policy makers can use in developing their intervention and resource allocation strategies. The applied model thus opens up possibilities in the application of hybrid deep learning techniques in taking prediction and management of epidemics to new levels.

关键词： COVID-19 Deep learning Accuracy Pandemics Predictive models Data models Real-time systems Vaccines Forecasting Long short term memory

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Robust Operation of Energy Communities in the Italian Incentive System

Robust Operation of Energy Communities in the Italian Incent...

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2023 IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2023

作者： Nammouchi, Amal Stentati, Marta Paoletti, Simone Kassler, Andreas Theocharis, Andreas Computer Science Department Karlstad University Karlstad Sweden Dipartimento di Ingegneria dell’Informazione e Scienze Matematiche Università di Siena Siena Italy Faculty of Computer Science Deggendorf Institute of Technology Deggendorf Germany Engineering and Physics Department Karlstad University Karlstad Sweden

ISBN: (纸本)9798350396782

In this paper, we address the optimal operation of energy communities, under energy production and consumption uncertainties. In the nominal case, the operational problem is formulated as the maximization of the profit of the community over a given time horizon. Inspired by the regulation adopted in Italy since 2020, the profit includes an incentive for the self-consumption realized at the community level in each time period. In the presence of uncertainties, we use a robust formulation aiming at maximizing the worst profit achievable when energy production and consumption deviate from their nominal values. The model results in a robust scheduling policy of battery charging/discharging, guaranteeing feasible operation of the community in all scenarios of the uncertainty set. On the other hand, numerical results show that the nominal scheduling policy may suffer from a high constraint violation probability. © 2023 IEEE.

关键词： Energy community profit maximization robust optimization

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Multi-Task Driven Multi-Level Dynamical Fusion for Single-Cell Multi-Omics Cell Type Annotation

Multi-Task Driven Multi-Level Dynamical Fusion for Single-Ce...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jiawei Chen, Shizhan Han, Zongbo Li, Wei Tang, Jijun Guo, Fei Tianjin University College of Intelligence and Computing Tianjin300072 China Nankai University College of Artificial Intelligence Tianjin518055 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Central South University School of Computer Science and Engineering Changsha410083 China

ISBN: (纸本)9798350386226

The emergence of single-cell multi-omics sequencing technology has enabled the simultaneous profiling of diverse omics data within individual cells. It offers a more comprehensive perspective on cellular phenotypes and heterogeneity. However, single-cell multi-omics data are inherently high-dimensional and heterogeneous. Due to technical limitations and scarce starting materials, the data are often affected by noise and dropout effects. To address these challenges, we propose a novel multitask driven multi-level dynamical fusion algorithm for single-cell multi-omics cell type annotation, named scMMDyn. Our approach incorporates reconstruction and classification auxiliary tasks to guide the training of trustworthy modules at both the feature and modality levels. It executes dynamical fusion during these stages and finally achieves cross-modality fusion via an attention mechanism. This method effectively mitigates data quality issues through reconstruction tasks and feature-level dynamical fusion while providing interpretability at both feature and modality levels. Experimental results across diverse single-cell multi-omics datasets show that our method surpasses existing approaches in cell type annotation. © 2024 IEEE.

关键词： cell type annotation multi-task mutli-level dynamical fusion single-cell multi-omics trustworthy

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A Fuzzy Logic-based Expert System for generating SCADA of Caterpillar engine 23

A Fuzzy Logic-based Expert System for generating SCADA of Ca...

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23rd International Middle East Power Systems Conference, MEPCON 2022

作者： Fahmy, Nerveen M.T. Abdul-Kader, H.M. El-Din, Ashraf Zein Menoufia University Faculty of Engineering Department of Electrical Power and Machines Shebin El Kom Egypt Menoufia University Faculty of Computer and Information Science Department of Computer and Information Shebin El Kom Egypt

ISBN: (纸本)9781665463638

The development of a fuzzy logic-based expert system module to predict the fault diagnosis of software modules under uncertainty and non-precision of data was proved in this study. The expert system's rule-base and database are tuned using a Fuzzy logic system. A supervision system enabled an intelligent decision-making system to achieve the goals of the degree of variation, failure type, and location. The use of a fuzzy logic-based expert system to generate SCADA for a caterpillar CAT 3126B has been proposed. The main object of a fuzzy knowledge-based system is implemented by simulating the caterpillar's parameters in the SIMULINK/MATLAB environment. An intelligent Scada system uses an expert system to detect events and situations, to reason with the events, and then come up with a response, such as giving advice or taking corrective actions. © 2022 IEEE.

关键词： Fuzzy logic

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Iot monitoring lathe machine performance

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Materials Today: Proceedings 2023年 80卷 3570-3574页

作者： Sharma, Rupak Sharma, Rajeev Prabha, Y.B. Rema Devi, S. Saxena, Pankaj Rajasanthosh kumar, T. Department of Computer Applications Faculty of Science& Humanities SRM Institute of Science & Technology NCR Campus Delhi NCR Campus Meerut-Delhi Road Modinagar UP Ghaziabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram India Department of English School of Liberal Arts and Special Education Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar District India R.B.S. Management Technical Campus Agra India Department of Mechanical Engineering Oriental Institute of Science & Technology Bhopal India

Machines have to operate effectively and free from faults in order to enhance productivity, increase quality, and decrease costs. With automated and flexible action, this is far more likely. An accurate identification of the underlying cause Causes of failure that need repairs and the time for restoration include: A lot of Various monitoring methods are available to track the experimentally-recorded machine health. Maintaining instruments and sensors. The vibration monitoring methods in this list. It is the right fit, and it has shown itself over time. Measuring vibrations in this job three different locations resembling the stock of a gun: headstock, tailstock. When conducting turning operation with mild steel, the tool post of the lathe machine is made of metal. Data that is gathered through sensors is sent straight to the cloud in this case, such as Ubidots IoT platform. via way of nodeMCU. Whether a task is impacted or a tool is impacted, mission failure has occurred. Thus, in either instance, early warning is provided by a signature, thus the development does not progress as quickly machining. If the tail-stock seems to be broken, and this appears to be a consistent problem, then the whole batch must be inspected for errors to be inspected closely is anything that has been created under such circumstances. An optimal decision-making procedure light may be thrown on CM so processes may be machine-wise instituted in a shop there. Tools while talking about lathes that are large, medium, or tiny. Overall it is a method which is easy but yet capable of benefitting people at all levels, from the absolute beginner to experts Floor productivity is aided by training elements that are almost unnecessary. © 2021

关键词： Lathes

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Prompt-Based Approach for Czech Sentiment Analysis

Prompt-Based Approach for Czech Sentiment Analysis

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Šmíd, Jakub Přibáň, Pavel Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS - New Technologies for the Information Society University of West Bohemia Faculty of Applied Sciences Czech Republic

ISBN: (纸本)9789544520922

This paper introduces the first prompt-based methods for aspect-based sentiment analysis and sentiment classification in Czech. We employ the sequence-to-sequence models to solve the aspect-based tasks simultaneously and demonstrate the superiority of our promptbased approach over traditional fine-tuning. In addition, we conduct zero-shot and few-shot learning experiments for sentiment classification and show that prompting yields significantly better results with limited training examples compared to traditional fine-tuning. We also demonstrate that pre-training on data from the target domain can lead to significant improvements in a zero-shot scenario. © 2023 Incoma Ltd. All rights reserved.

关键词： Sentiment analysis

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