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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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ChatGPT as a Negotiator: An Analysis of its Adherence with Proportionality and Equality 7

ChatGPT as a Negotiator: An Analysis of its Adherence with P...

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7th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Aksoy, Meltem Tsishetska, Veronika Computer Science Research Center Trustworthy Data Science and Security Technical University Dortmund Dortmund Germany Computer Science Technical University Dortmund Dortmund Germany

ISBN: (纸本)9798331529314

This study examines the adherence of ChatGPT (GPT-3.S and GPT-4) to fairness principles, specifically proportionality and equality, in negotiation scenarios. Three distinct negotiation contexts were explored: work-study program funding, company sale proceeds division, and employee bonus allocation. The models were prompted to prioritize proportionality, equality, or no specific fairness principle to assess how they respond in different ethical frameworks. A combination of qualitative and quantitative methods, including dialogue analysis, sentiment tracking, and fairness scoring, was used to evaluate their ne-gotiation behaviors. Results indicate that both models tend to favor proportionality by default, with GPT-4 showing greater adaptability compared to GPT-3.S. When explicitly directed to prioritize equality, the models followed these instructions but maintained a largely assertive negotiation style with limited engagement in dynamic exchanges. Sentiment analysis revealed that both models adopted increasingly positive tones as nego-tiations progressed. However, both versions were susceptible to prompt manipulation, potentially compromising fairness in some outcomes. This research highlights the potential of LLMs like ChatGPT in automating fair negotiations, though improvements in consistency, adaptability, and safeguards against manipulation are necessary for more robust, ethical applications in real-world scenarios. © 2024 IEEE.

关键词： Economic and social effects

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A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detection 2

A Hybrid CNN-SVM Approach for Malaria in Blood Cell Detectio...

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2nd International Conference on Software Engineering and Information Technology, ICoSEIT 2024

作者： Karnyoto, Andrea Stevens Isnan, Mahmud Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350317503

There is still a severe malaria problem worldwide, particularly in regions with limited access to diagnostic tools. It is crucial to develop a system for detecting malaria in blood cells. This paper presents a hybrid Convolutional Neural Network (CNN), and as a classifier, we use a Support Vector Machine (SVM) framework for the automated detection of malaria parasites in blood cell images. The proposed system leverages the strengths of CNNs in feature extraction and representation learning from images, combined with the discriminative power of SVMs for classification. Initially, CNN extracts intricate features from blood cell images, capturing essential patterns indicative of malaria infection. Subsequently, the extracted features are used to train an SVM classifier, enabling accurate discrimination between parasitized and uninfected blood cells. Experimental dataset evaluations were obtained from the Lister Hill National center for Biomedical Communications website of the National Library of Medicine. The proposed model achieves a better f1-score, outperforming individual CNN or SVM models, around 0.015 compared to individual CNN models and 0.27 compared to individual SVM models. This hybrid CNN-SVM methodology offers a promising solution for accurately and efficiently detecting malaria parasites in blood cell images. © 2024 IEEE.

关键词： Support vector machines

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Implementation of Fine-Tuned BERT for Enzyme Classification Based on Gene Ontology 11

Implementation of Fine-Tuned BERT for Enzyme Classification ...

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11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

作者： Henry, Matthew Martianus Kenneth, Christian Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331520311

Enzymes are biocatalysts with vital roles in biological functions and many industrial applications. Diverse enzymes are classified using Enzyme Commission (EC) nomenclature, making differentiation challenging. On the other hand, another biological information, gene ontology (GO), can describe the biological aspects of enzymes, covering related biological processes (BP), molecular functions (MF), and their locations within cells (CC). This study proposes a novel EC class and subclass classification of enzymes within the ontology subclass based on their GO semantics using a Bidirectional Encoder Representation of Transformer (BERT). The BERT model is first fine-tuned using the preprocessed GO term name and definition, with the enzymes in each ontology class (BP, MF, or CC) are also divided based on how the GO assigned, either through manual annotation (NONIEA) or electronically inferred (IEA). BERT successfully obtained 0.93, 0.60, 0.99, 0.90, 0.40, and 0.35 F1 scores during fine-tuning for BP IEA, BP NONIEA, MF IEA, MF NONIEA, CC IEA, and CC NONIEA, respectively. On the test set, the fine-tuned BERT significantly outperformed GOntoSim, a framework to calculate semantic similarity based on classical information theory, in EC class classification across all metrics with less inference time in all ontology subclass. Expanded further to the EC subclass, BERT can classify the enzyme on the EC subclass level in BP IEA and MF IEA ontology subclass. However, longer epochs are needed in fine-tuning. This result shows that the names and definitions of GO terms are distinguishable features in classifying enzymes as an alternative to the information content approach. © 2024 IEEE.

关键词： Gene Ontology

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Age-of-Information-Aware Federated Learning

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Journal of computer science & Technology 2024年第3期39卷 637-653页

作者：徐殷肖明军吴晨吴杰周津锐孙贺 Student Member CCF 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Suzhou Institute for Advanced Research University of Science and Technology of ChinaSuzhou 215123China School of Data Science University of Science and Technology of ChinaHefei 230026China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122U.S.A. CCF IEEE

Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private datasets to the central *** most existing research where the local datasets of clients are assumed to be unchanged over time throughout the whole FL process,our study addresses such scenarios in this paper where clients’datasets need to be updated periodically,and the server can incentivize clients to employ as fresh as possible datasets for local model *** primary objective is to design a client selection strategy to minimize the loss of the global model for FL loss within a constrained *** this end,we introduce the concept of“Age of Information”(AoI)to quantitatively assess the freshness of local datasets and conduct a theoretical analysis of the convergence bound in our AoI-aware FL *** on the convergence bound,we further formulate our problem as a restless multi-armed bandit(RMAB)***,we relax the RMAB problem and apply the Lagrangian Dual approach to decouple it into multiple ***,we propose a Whittle’s Index Based Client Selection(WICS)algorithm to determine the set of selected *** addition,comprehensive simulations substantiate that the proposed algorithm can effectively reduce training loss and enhance the learning accuracy compared with some state-of-the-art methods.

关键词： federated learning Age of Information restless multi-armed bandit Whittle’s index

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Towards real-time ML-based DDoS detection via cost-efficient window-based feature extraction

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science China(Information sciences) 2023年第5期66卷 114-129页

作者： Haibin LI Yi ZHAO Wenbing YAO Ke XU Qi LI Department of Computer Science and Technology Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Institute for Network Sciences and Cyberspace Tsinghua University

Distributed denial of service(DDoS) detection is still an open and challenging problem. In particular, sophisticated attacks, e.g., attacks that disguise attack packets as benign traffic always appear, which can easily evade traditional signature-based methods. Due to the low requirements for computing resources compared to deep learning, many machine learning(ML)-based methods have been realistically deployed to address this issue. However, most existing ML-based DDo S detection methods are highly dependent on the features extracted from each flow, which incur remarkable detection delay and computation overhead. This article investigates the limitations of typical ML-based DDo S detection methods caused by the extraction of flow-level features. Moreover, we develop a cost-efficient window-based method that extracts features from a fixed number of packets periodically, instead of per flow, aiming to reduce the detection delay and computation overhead. The newly proposed window-based method has the advantages of well-controlled overhead and wide support of common routers due to its simplicity and high efficiency by design. Through extensive experiments on real datasets, we evaluate the performance of flow-based and window-based *** experimental results demonstrate that our proposed window-based method can significantly reduce the detection delay and computation overhead while ensuring detection accuracy.

关键词： DDoS attack machine learning feature extraction detection delay cost-efficiency

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Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

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High-Confidence Computing 2024年第4期4卷 91-104页

作者： Shabab Intishar Rahman Shadman Ahmed Tasnim Akter Fariha Ammar Mohammad Muhammad Nayeem Mubasshirul Haque Sriram Chellappan Jannatun Noor Department of Computer Science and Engineering BRAC UniversityDhaka 1212Bangladesh Department of Computer Science and Engineering University of South FloridaFL 33620USA Computing for Sustainability and Social Good(C2SG)Research Group Department of Computer Science and EngineeringSchool of Data and SciencesBRAC UniversityDhaka 1212Bangladesh

By analyzing data gathered through Online Learning(OL)systems,data mining can be used to unearth hidden relationships between topics and trends in student ***,in this paper,we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system *** our implementation,we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%.By focusing on marks below this threshold,we aim to identify and establish associations based on the patterns of weakness observed in the past ***,we leverage K-means clustering to provide instructors with visual representations of the generated *** strategy aids instructors in better comprehending the information and associations produced by the *** clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights,enabling them to support the verification of the relationship between *** can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their *** paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

关键词： Recommendation system Clustering F-P growth Apriori Associative pattern E-learning sphere Online learning

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Hidayat, Alam Ahmad Nirwantono, Rudi Isnan, Mahmud Trinugroho, Joko Pebrianto Pardamean, Bens Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Binus Graduate Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features. © 2023 IEEE.

关键词： Proteins

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