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International Conference on Contemporary computing and Informatics (IC3I)

作者： Lalitha Krishnasamy Kiruthika M Manjunatha Swamy C Vigneshwaran T Sharon Roji Priya C D. Vijay Anand Department of CSE School of Engineering and Technology Christ University Bengaluru India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Information Technology Kongu Engineering College Erode India

Numerous cybercriminals are active in the online realm, carrying out cyber-crimes according to predetermined and preplanned agendas. Cyberbullying, which was formerly limited to physical limits, has now expanded online as a result of technology advancements. One type of cyberbullying is denigration or insult. The cyberbullying cases are in exponential rise in social media as per the reports of Computer Emergency Team by Sri Lanka. Insulting words are changeable in dynamic and the same terminology may have numerous meanings depending on the context. Bullying cannot be defined just because a statement comprises such a term. As a result, when classifying comments, standard keyword detecting approaches are insufficient. Other languages also may have dealt with this issue by utilizing lexical databases like WordNet, which might give synonyms as well as homonyms for words. Because no adequate lexical database mainly for the English language has been built, recognizing a word like bullying is difficult. As a result, employed rules to solve the problem. Facebook comments containing profanity were gathered, outliers were eliminated, and the remaining messages were pre-processed. Five feature extraction rules were employed to assess insult in the text. Following that, used the Support Vector Machine (SVM) technique. Using an F1-score of 85%, the findings demonstrate that when compared to existing works, SVM performs better. The focus on English language cyberbully identification, which has never been addressed earlier, distinguishes this study.

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IoT-Enabled Precision Irrigation for Coconut Plantations: Enhancing Growth and Sustainability

IoT-Enabled Precision Irrigation for Coconut Plantations: En...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： M.Jaganmohan Reddy V. Ramu T. Grace Shalini R. Vincysahaba M. Devaki A. Athiraja Department of CSE (AIML) Malla Reddy Engineering College Secunderabad Telangana India Department of CSE(AI&ML) Kakatiya Institute of Technology and Science Hanamkonda Telangana India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science St.Joseph college of engineering Chennai Tamil Nadu India Department of EEE Velammal college of engineering and technology Madurai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

In agriculture, precision irrigation plays a pivotal role in optimizing crop growth and resource utilization. In this study, must proposean Internet of Things based irrigation remote monitoring system tailored for enhancing coconut plant growth. The system integrates sensor technology, Internet of Things connectivity, and data analytics to provide real-time monitoring and control of irrigation parameters such as soilmoisture, temperature, and humidity. Leveraging a network of sensors deployed in the coconut plantation, the system collects and analyzes environmental data to optimize irrigation schedules and ensure optimal growing conditions for coconut plants. Through a user-friendly interface accessible via web or mobile applications, farmers can remotely monitor the status of their plantation and adjust irrigation settings as ***, the system incorporates predictive analytics capabilities to anticipate irrigation needs based on historical data and weather forecasts, thereby enhancing water efficiency and crop productivity. Field trials demonstrate the effectiveness of this Internet of Things based irrigation system in improving coconut plant growth while reducing water consumption and operational costs. This findings underscore the potential of Internet of Things technologies in revolutionizing agricultural practices, facilitating sustainable crop management, and enhancing food security in coconut-producing regions.

关键词： Temperature sensors Productivity Irrigation Costs Crops Plantations Food security Internet of Things Sustainable development Predictive analytics

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Deep Fake BERT: Efficient Online Fake News Detection System

Deep Fake BERT: Efficient Online Fake News Detection System

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Networking and Communications (ICNWC), International Conference on

作者： M Kanchana Vel Murugesh Kumar T.P Anish. Pv Gopirajan Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering R.M.K Engineering CollegeKavaraipettaiGummudipoondi Taluk Thiruvallur District Chennai India Department of Computer Science and Engineering R.M.K College of Engineering and Technology RSM Nagar Puduvoyal Gummidipoondi Taluk Thiruvallur District Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur campus Chennai India

The newscast system has shifted from conventional print to online media platforms in the current computing era. As a result, online media platforms enable us to absorb information more quickly and with fewer editorial constraints, and false information is disseminated at an extraordinary rate and on a massive scale. Many practical algorithms for identifying fake News have recently been created, which use unidirectional text sequence analysis. News and social context-level information were encoded using sequential neural networks. As a result, a bidirectional training strategy is capable of enhancing classification. This paper proposed Deep Fake BERT, a new model for identifying bogus News in online media. The model uses a BERT-based deep learning technique by integrating multiple simultaneous modules into a single-layer DCNN with various kernel filter sizes and strides. This combination can handle ambiguity, the most challenging aspect of natural language comprehension. This approach used classification methods such as Naive Bayes, Feed Forward Neural Networks, and LSTM, and prediction results were compared. Based on the comparison, the proposed model yields a classification accuracy is 99.25% to the existing methods.

关键词： Training Deep learning Deepfakes Sequences Neural networks Bit error rate Natural languages

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scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics

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BRIEFINGS IN BIOINFORMATICS 2024年第3期25卷 bbae112-bbae112页

作者： Zheng, Chuanyang Wang, Yixuan Cheng, Yuqi Wang, Xuesong Wei, Hongxin King, Irwin Li, Yu Department of Computer Science and Engineering CUHK Hong Kong SAR China College of Computing Georgia Institute of Technology Atlanta GA USA MLR Lab Southern University of Science and Technology The CUHK Shenzhen Research Institute Hi-Tech Park Nanshan Shenzhen 518057 China Institute for Medical Enginering and Science Massachusetts Institute of Technology Cambridge MA USA Wyss Institute for Biologically Inspired Engineering Harvard University Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA

Single-cell RNA sequencing has achieved massive success in biological research fields. Discovering novel cell types from single-cell transcriptomics has been demonstrated to be essential in the field of biomedicine, yet is time-consuming and needs prior knowledge. With the unprecedented boom in cell atlases, auto-annotation tools have become more prevalent due to their speed, accuracy and user-friendly features. However, existing tools have mostly focused on general cell-type annotation and have not adequately addressed the challenge of discovering novel rare cell types. In this work, we introduce scNovel, a powerful deep learning-based neural network that specifically focuses on novel rare cell discovery. By testing our model on diverse datasets with different scales, protocols and degrees of imbalance, we demonstrate that scNovel significantly outperforms previous state-of-the-art novel cell detection models, reaching the most AUROC performance(the only one method whose averaged AUROC results are above 94%, up to 16.26% more comparing to the second-best method). We validate scNovel's performance on a million-scale dataset to illustrate the scalability of scNovel further. Applying scNovel on a clinical COVID-19 dataset, three potential novel subtypes of Macrophages are identified, where the COVID-related differential genes are also detected to have consistent expression patterns through deeper analysis. We believe that our proposed pipeline will be an important tool for high-throughput clinical data in a wide range of applications.

关键词： novel rare cell discovery single-cell analysis neural network

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Tempered Multifidelity Importance Sampling for Gravitational Wave Parameter Estimation

arXiv

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arXiv 2024年

作者： Saleh, Bassel Zimmerman, Aaron Chen, Peng Ghattas, Omar Oden Institute for Computational Engineering and Sciences The University of Texas at Austin AustinTX78712 United States Center for Gravitational Physics The University of Texas at Austin AustinTX78712 United States College of Computing Georgia Institute of Technology AtlantaGA30332 United States

Estimating the parameters of compact binaries which coalesce and produce gravitational waves is a challenging Bayesian inverse problem. Gravitational-wave parameter estimation lies within the class of multifidelity problems, where a variety of models with differing assumptions, levels of fidelity, and computational cost are available for use in inference. In an effort to accelerate the solution of a Bayesian inverse problem, cheaper surrogates for the best models may be used to reduce the cost of likelihood evaluations when sampling the posterior. Importance sampling can then be used to reweight these samples to represent the true target posterior, incurring a reduction in the effective sample size. In cases when the problem is high dimensional, or when the surrogate model produces a poor approximation of the true posterior, this reduction in effective samples can be dramatic and render multifidelity importance sampling ineffective. We propose a novel method of tempered multifidelity importance sampling in order to remedy this issue. With this method the biasing distribution produced by the low-fidelity model is tempered, allowing for potentially better overlap with the target distribution. There is an optimal temperature which maximizes the efficiency in this setting, and we propose a low-cost strategy for approximating this optimal temperature using samples from the untempered distribution. In this paper, we motivate this method by applying it to Gaussian target and biasing distributions. Finally, we apply it to a series of problems in gravitational wave parameter estimation and demonstrate improved efficiencies when applying the method to real gravitational wave detections. Copyright © 2024, The Authors. All rights reserved.

关键词： Gravity waves

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Two Physics-Based Schwarz Preconditioners for Three-Temperature Radiation Diffusion Equations in High Dimensions

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Communications in computational Physics 2022年第8期32卷 829-849页

作者： Xiaoqiang Yue Jianmeng He Xiaowen Xu Shi Shu Libo Wang National Center for Applied Mathematics in Hunan Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationHunan Key Laboratory for Computation and Simulation in Science and EngineeringXiangtan UniversityXiangtan 411105P.R.China Laboratory of Computational Physics Institute of Applied Physics and ComputationalMathematicsBeijing 100094P.R.China College of Information Science and Technology JinanUniversityGuangzhou 510632P.R.China

We concentrate on the parallel,fully coupled and fully implicit solution of the sequence of 3-by-3 block-structured linear systems arising from the symmetrypreserving finite volume element discretization of the unsteady three-temperature radiation diffusion equations in high *** this article,motivated by[***,***,***,SIAM *** ***.33(2012)653–680]and[***,***,***,***.442(2021)110513],we aim to develop the additive and multiplicative Schwarz preconditioners subdividing the physical quantities rather than the underlying domain,and consider their sequential and parallel implementations using a simplified explicit decoupling factor approximation and algebraic multigrid subsolves to address such linear ***,computational efficiencies and parallel scalabilities of the proposed approaches are numerically tested in a number of representative real-world capsule implosion benchmarks.

关键词： Radiation diffusion equations Schwarz methods algebraic multigrid parallel and distributed computing

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Autonomous Decision-Making in Cyber-Physical Systems Using Double Deep Q-Learning and Multi-Agent Collaboration 3

Autonomous Decision-Making in Cyber-Physical Systems Using D...

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3rd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2025

作者： Divyabarathi, P. Anbarasan, R. Vasanthi, R. Leena, B. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Tamil Nadu Erode India Department of Information Technology KGiSL Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Bharathidasan Engineering College Tamil Nadu Thirupathur India Department of Electronics and Communication Engineering KGiSL Institute of Technology Tamil Nadu Coimbatore India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Networking and Communications SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331543891

The autonomous decision-making capabilities of cyber-physical systems (CPS) establish key elements that optimize the management of resources while distributing tasks and maximizing system operational efficiency. Deep Q-Learning (DQN) represents one approach along with Twin Delayed Deep Deterministic Policy Gradient (TD3) and Multi-Agent Proximal Policy Optimization (PPO) that demonstrates capability in handling decision tasks. The existing models face multiple implementation challenges including inaccurate Q-value estimations weak multi-agent relationships and restricted scalability for dynamic systems. The inadequate performance of present methods for energy efficiency and latency alongside poor collaborative coordination led to the necessity of developing a new approach. The new framework DDQN-MAC takes double-deep Q-Learning and combines it with multi-agent collaboration technology to enhance decision-making abilities specific to challenging environments. The method solves overestimation bias through separate action selection and evaluation processes that use multi-agent collaboration for dynamic task distribution and resource control. The designed model demonstrates enhanced performance in multiple critical evaluation measures. Benchmarks indicate that DDQN-MAC attained a superior completion rate of 95.4% while demonstrating reduced latency performance at 12.5 ms versus the other methods DQN (18.7 ms) and Multi-Agent PPO (92.3%). The research provides fundamental groundwork to guide upcoming investigations in hybrid model development while advancing complex multi-agent system capabilities for enhanced autonomous navigational choices within variable operational environments. © 2025 IEEE.

关键词： Risk perception

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An Improved Dung Beetle Optimizer for Random Forest Optimization 6

An Improved Dung Beetle Optimizer for Random Forest Optimiza...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Tan, Lianghao Liu, Xiaoyi Liu, Dong Liu, Shubing Wu, Weixi Jiang, Huangqi W. P. Carey School of Business Arizona State University Tempe United States Ira A. Fulton Schools of Engineering Arizona State University Tempe United States Yale University Department of Computer Science New Haven United States University of North Carolina at Chapel Hill Department of Computer Science Chapel Hill United States Tandon School of Engineering New York University New York United States College of Computing Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9798331541750

To improve the convergence speed and optimization accuracy of the Dung Beetle Optimizer (DBO), this paper proposes an improved algorithm based on circle mapping and longitudinal-horizontal crossover strategy (CICRDBO). First, the Circle method is used to map the initial population to increase diversity. Second, the longitudinal-horizontal crossover strategy is applied to enhance the global search ability by ensuring the position updates of the dung beetle. Simulations were conducted on 10 benchmark test functions, and the results demonstrate that the improved algorithm performs well in both convergence speed and optimization accuracy. The improved algorithm is further applied to the hyperparameter selection of the Random Forest classification algorithm for binary classification prediction in the retail industry. Various combination comparisons prove the practicality of the improved algorithm, followed by SHapley Additive exPlanations (SHAP) analysis. © 2024 IEEE.

关键词： Random forests

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Autonomous Decision-Making in Cyber-Physical Systems Using Double Deep Q-Learning and Multi-Agent Collaboration

Autonomous Decision-Making in Cyber-Physical Systems Using D...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Divyabarathi P Anbarasan R. Vasanthi R B. Leena Akshya J M. Sundarrajan Mani Deepak Choudhry Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Information Technology KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Bharathidasan Engineering College Thirupathur Tamil Nadu India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

关键词： Q-learning Decision making Collaboration Estimation Cyber-physical systems Dynamic scheduling Energy efficiency Resource management Vehicle dynamics Optimization

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Revolutionizing Aquaculture Practices: IoT and Deep Learning-based Remote Monitoring for Sustainable Fish Farming

Revolutionizing Aquaculture Practices: IoT and Deep Learning...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： S. Prince Chelladurai G.Susan Shiny S.V Jeevitha P Swapna R. Pavithra S Arulraj Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Department of information technology Sethu institute of Technology Pulloor Kariapatti Virudhunagar Tamil Nadu Department of information technology Bannari Amman Institute of Technology Erode India Department of CSE Velammal college of engineering and technology Madurai Department of ECE Rathinam Technical Campus Coimbatore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Aquaculture plays a vital role in meeting the growing demand for seafood while addressing sustainability concerns in fisheries. To optimize fish growth and ensure efficient resource utilization an innovative IoT and Deep Learning-based remote monitoring system is proposed tailored for aquaculture operations. Our system integrates sensor technology, IOT connectivity, and Deep Learning algorithms to provide real-time monitoring and control of key environmental parameters such as water temperature, pH levels, oxygen concentration, and feeding behavior. By leveraging a network of sensors deployed in aquaculture facilities, our system collects and analyzes environmental data to optimize aquaculture practices and ensure optimal conditions for fish growth. Through a user-friendly interface accessible via web or mobile applications, aqua-culturists can remotely monitor the status of their facilities and adjust operational parameters as needed. Additionally, our system utilizes Deep Learning algorithms to analyze historical data and predict optimal feeding schedules, thereby enhancing feed efficiency and fish growth rates. Field trials demonstrate the effectiveness of our IOT and Deep Learning-based monitoring system in improving fish health and productivity while reducing environmental impacts and operational costs. Our findings highlight the potential of IoT and Deep Learning technologies in revolutionizing aquaculture practices, promoting sustainability, and enhancing food security in fisheries

关键词： Deep learning Temperature sensors Productivity Schedules Fisheries Real-time systems Feeds Sustainable development Remote monitoring Water resources

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