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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Samaniego, Christian Cuba Wallace, Emily Blanchini, Franco Franco, Elisa Giordano, Giulia Carnegie Mellon University Department of Computational Biology PittsburghPA United States Michigan University Ann ArborMI United States University of Udine Department of Mathematics Computer Science and Physics Italy University of California Department of Mechanical and Aerospace Engineering Los AngelesCA United States University of Trento Department of Industrial Engineering Italy

ISBN: (纸本)9798350316339

The engineering of molecular programs capable of processing patterns of multi-input biomarkers holds great potential in applications ranging from in vitro diagnostics (e.g., viral detection, including COVID-19) to therapeutic interventions (e.g., discriminating cancer cells from normal cells). For this reason, mechanisms to design molecular networks for pattern recognition are highly sought after. In this work, we explore how enzymatic networks can be used for both linear and nonlinear classification tasks. By leveraging steady-state analysis and showing global stability, we demonstrate that these networks can function as molecular perceptrons, fundamental units of artificial neural networks - capable of processing multiple inputs associated with positive and negative weights to achieve linear classification. Furthermore, by composing orthogonal enzymatic reactions, we show that multi-layer networks can be constructed to achieve nonlinear classification. © 2024 IEEE.

关键词： Cell engineering

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An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第5期36卷 1571-1583页

作者： Reem Alkanhel Kalaiselvi Chinnathambi C.Thilagavathi Mohamed Abouhawwash Mona A.Al duailij Manal Abdullah Alohali Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the *** energy-efﬁcient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difﬁcult areas of *** every sensor node has aﬁnite amount of *** power is the most signiﬁcant source in the *** is a well-known technique for enhan-cing the power feature in *** the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing *** using distributed data transmission modiﬁcation,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire *** begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy *** Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efﬁcient Cluster Head(CH).It also improves the network’s longevity and optimizes the *** a result of the study’sﬁndings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay.

关键词： Clustering energy consumption genetic algorithm multi swarm optimization adaptive hierarchical clustering routing cluster head

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Fine- Tuning Pre-trained Models for Aspect-Based Sentiment Analysis of Amazon Food Reviews

Fine- Tuning Pre-trained Models for Aspect-Based Sentiment A...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Sridhar, D. Manikandan, K.B. Padma Priya, R.S. Pradeepa, D. Vasumathi, B. Shankar, R. Alliane University Alliance College of Engineering and Design Central Campus Karnataka Bangalore India Hyderabad India Vignan's Foundation for Science Technology and Research Department of Information Technology and Computer Applications Guntur India Reva University School of Computer Science and Applications Bangalore India Brainware University Department of Computational Sciences West Bengal India Bharathiar University Research in Commerce Coimbatore India

ISBN: (纸本)9798331513894

The process of classifying consumer responses to a particular product review as neutral, negative, or positive is known as sentiment analysis. Product owners can utilize sentiment analysis for assistance in them decide whether to continue producing their product, increase production, or eliminate production altogether. As a result, they will gain a deeper comprehension of how customers feel about their products. The method used to determine the opinions expressed in the review regarding a specific feature or aspect of the product is called Aspect Based Sentiment Analysis, or ABSA. Numerous machine learning and natural language processing models are currently available for ABSA. The inability to precisely identify and categories the views expressed towards particular features is a major drawback of the aspect-based sentiment analysis (ABSA) techniques presently in usage for Amazon food assessment tasks. The Hybrid Contextual Aspect-Based Sentiment Analyzer (HCABSA), a hybridized framework for aspect-based sentiment analysis, is developed in this research to accurately assess the sentiment of Amazon food reviews through a number of processes. This research starts by collecting dataset for Amazon food reviews from Kaggle, preprocessing the data to eliminate noise and irrelevant information, and then using POS tagging to identify the specific aspects and sentiment words related to those aspects, which are nouns and adjectives in the review using the Lexicon based approach. In the following stage, the frequency-based candidate extraction method is utilized to pick the characteristics that are used to determine the product's internal and exterior features. The raw data is then transformed into features that the machine learning models can access using the Bag of Words feature extraction process. The Gradient Boosting Decision Tree technique is then used to categories the sentiment as either positive or negative. Using performance metrics like accuracy, precision, recall,

关键词： Decision trees

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An Automatic Deep Neural Network Model for Fingerprint Classification

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Intelligent Automation & Soft Computing 2023年第5期36卷 2007-2023页

作者： Amira Tarek Mahmoud Wael AAwad Gamal Behery Mohamed Abouhawwash Mehedi Masud Hanan Aljuaid Ahmed Ismail Ebada Department of Computer Science Faculty of SciencePort Said UniversityPort SaidEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaEgypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI 48824USA Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman University(PNU)P.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaEgypt

The accuracy ofﬁngerprint recognition model is extremely important due to its usage in forensic and securityﬁ***ﬁngerprint recognition system has particular network architecture whereas many other networks achieve higher *** solve this problem in a uniﬁed model,this paper proposes a model that can automatically specify ***,it is called an automatic deep neural net-work(ADNN).Our algorithm can specify the appropriate architecture of the neur-al network used and some signiﬁcant parameters of this *** parameters are the number ofﬁlters,epochs,and *** guarantees the high-est accuracy by updating itself until achieving 99%accuracy then it stops and out-puts the ***,this paper proposes an end-to-end methodology for recognizing a person’s identity from the inputﬁngerprint image based on a resi-dual convolutional neural *** is a complete system and is fully automated whether in the features extraction stage or the classiﬁcation *** goal is to automate thisﬁngerprint recognition system because the more automatic the sys-tem is,the more time and effort it *** model also allows users to react by inputting the initial values of these ***,the model updates itself until itﬁnds the optimal values for the parameters and achieves the best *** advantage of our algorithm is that it can recognize people from their thumb and otherﬁngers and its ability to recognize distorted *** algo-rithm achieved 99.75%accuracy on the publicﬁngerprint dataset(SOCOFing).This is the best accuracy compared with other models.

关键词： Automatic system ﬁngerprint classiﬁcation residual networks deep learning

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Multiclass Classification of EEG Motor Imagery Signals Based on Transfer Learning 8

Multiclass Classification of EEG Motor Imagery Signals Based...

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8th International Conference on Applied System Innovation, ICASI 2022

作者： Chen, Chun-Yu Wang, Wei-Jen Chen, Chun-Chuan National Central University Laboratory of Computational Neuroscience Department of Biomedical Sciences and Engineering Taoyuan Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9781665496506

Multiclass Classification of EEG signal is essential for brain computer interface (BCI) applications but extremely time consuming. We proposed a subject-weighted adaptive transfer learning method in conjunction with MLP and CNN classifiers for fast classification of multiclass EEG *** results show that CNN generally outperforms MLP in this multi-class classification. The use of transfer learning is efficient for building the predictive model without decreasing the accuracy and 2D CNN is more robust to between-subject variabilities. © 2022 IEEE.

关键词： Deep learning

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Solar Wind Density Forecasting with U-Net and LSTM-based Neural Networks 8

Solar Wind Density Forecasting with U-Net and LSTM-based Neu...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Iacobelli, Emanuele Fiani, Francesca Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Forecasting changes in solar wind properties accurately is crucial for predicting space weather, as it significantly impacts the majority of space operations and the telecommunication system. To meet this challenge, we introduce an architecture that combines U-Net’s capabilities for segmenting coronal holes from high-resolution sun images with the predictive abilities of Long Short-Term Memory (LSTM) and ConvLSTM models. This architecture predicts solar wind density using sun surface images obtained from the AIA 193 Å dataset (provided by NASA) and historical electron and proton density data from the OMNI and ELM2 datasets (also provided by NASA), covering the entire year 2012. Our findings demonstrate the system’s ability to generate reliable coronal hole segmentation maps and achieve good accuracy in forecasting solar wind density. © 2023 Copyright for this paper by its authors.

关键词： NASA

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Security Analysis and Improvement of Authenticated Key Agreement Protocol for Remote Patient Monitoring IoMT

Security Analysis and Improvement of Authenticated Key Agree...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Gautam, Deepika Thakur, Garima Obaidat, Mohammad S. Hsiao, Kuei-Fang Kumar, Pankaj Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics HP Dharamshala176215 India School of Computer and Communication Engineering University of Science and Technology Beijing China School of Computing SRM University Department of Computational Intelligence TN Kattankulathur India School of Engineering The Amity University UP 201301 India

ISBN: (纸本)9798350349832

The Internet of Medical Things (IoMT) is a network of medical devices, hardware infrastructure, and software that accumulates real-Time data through remote monitoring and minimizes doctor-patient interaction, enhancing the quality of remedies. However, the facile availability and accessibility of data over the web have given rise to serious information security and credibility issues in the IoMT. Recently, Chen et al. [1] proposed a provably secure authenticated key agreement protocol for remote patient monitoring in IoMT. However, after revisiting their scheme, we found security flaws such as user impersonation, ephemeral secret leakage attacks, denial-of-service attacks, and session key disclosure attacks. Therefore, this paper presents cryptanalysis as well as an improvement of Chen et al. 's scheme that strives to furnish protocols with robust security in opposition to the mentioned attacks. The security and effectiveness are displayed through informal analysis and evaluation with other existing state-of-The-Art metrics like computational and commu-nication cost. At last, the findings project the superiority of the proposed improved scheme. © 2024 IEEE.

关键词： Authentication Protocol

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Blending Spline Surfaces Over Polygon Mesh and Their Application to Isogeometric Analysis

SSRN

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SSRN 2023年

作者： Kravetc, Tatiana Department of Computer Science and Computational Engineering Faculty of Engineering Science and Technology UiT The Arctic University of Norway Norway

Finite elements are allowed to be of a shape suitable for the specific problem. This choice defines thereafter the accuracy of the approximated solution. Moreover, flexible element shapes allow for the construction of an arbitrary domain topology. Polygon meshes are a common representation of the domain that cover any choice of the finite element *** an alternative tool for modeling and analysis, blending spline surfaces support representation on polygon grids. The blending splines have a hierarchical structure, which is obtained by generating local surfaces that cover each node support and then blended with a special type of basis functions. This type of splines in their tensor product form is suitable for application to isogeometric analysis problems. A more general representation constructed on polygonal elements can be used on a wider range of domain topology in comparison with tensor product *** this paper we introduce a novel approach to constructing curvilinear polygon meshes in the blending spline representation in application to the isogeometric analysis context. © 2023, The Authors. All rights reserved.

关键词： Blending

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Innovations in Natural Language Processing through Enhanced Linguistic Model Accuracy and Efficiency using Advanced Reinforcement Learning Techniques 2

Innovations in Natural Language Processing through Enhanced ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Kalaiselvi, K. Shanmugam, Raju Tamilselvan, S. Manikandan, S.P. Rajasekar, P. Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India Unitedworld School of Computational Intelligence Karnavati University Gujarat India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Chennai India School of Engineering and Technology CMR UNIVERSITY Karnataka Bengaluru India Department of ECE Sri Krishna College of Technology Kovaipudur Coimbatore India

ISBN: (纸本)9798350383867

The paper will be focusing on combining sophisticated reinforcement learning approaches with natural language processing (NLP) platforms to step up the accuracy, efficiency, and adaptability of the linguistic models. The methodology approach is based on stepwise creation of efficient basic model architectures which is combined with formulation of tasks as RL problems and careful selection of appropriate algorithms and training processes. In our study on the development of an advanced AI model which took varying evaluation metrics, it is now apparent through the comprehensive evaluation of the same, that our model's performance on different evaluation metrics was significantly better than that of the baseline approaches. For instance, this technique leads to better precision and operation speed, allowing NLP systems to undertake more complex linguistic tasks with higher rate of precision and faster inferential speeds. Furthermore, this approach facilitates the model adaptation process which helps in accommodating a hybrid intelligence environment where models are able to evolve in changing language contexts and domains. Interpretability assessment is focused on producing not only the transparency of the described method but also the ability for the user to understand the reasoning of the model and the decision-making processes. Smart cities also undertake a set of ethical considerations that encompass the significance of regards to bias mitigation, fairness awareness and transparency to build equitable and trustworthy outcomes in the real-world implementation. In total, research done shows the possibility for advanced reinforcement learning techniques to have a significant impact to NLP, thus creating innovative NLP models capable of ensuring that they are diverse, adaptable and have necessary measures of ethics as the needs are changing. © 2024 IEEE.

关键词： Adversarial machine learning

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A Keyphrase-Centric Search Engine for Scientific Papers 23

A Keyphrase-Centric Search Engine for Scientific Papers

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Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation

作者： Avishek Lahiri Debarshi Kumar Sanyal Imon Mukherjee School of Mathematical and Computational Sciences Indian Association for the Cultivation of Science India Department of Computer Science and Engineering IIIT Kalyani India

ISBN: (纸本)9798400716324

The ever increasing interest and participation in scientific research and development has been a major contributor towards the exponential increase of textual and multi-modal scientific data on the Web over the past few years. It is imperative for scientists to keep track of the latest development in their respective area of research, but the ever-expanding landscape of research publications makes it quite challenging. Automating the parsing, retrieval, and understanding of this literature through state-of-the-art NLP and IR tools is the need of the hour. The goal of this doctoral work will be to develop new tools singularly for the low-resource scientific domain. We propose to contribute to this field by not only developing deep learning solutions for these tasks, but also providing the NLP and IR community with unique challenging datasets wherever necessary.

关键词： Citation intent classification Entity linking Keyphrase extraction Knowledge graph Named entity recognition

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