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Contemporary computing and Communications (InC4), IEEE International Conference on

作者： Chaithanyaka Yeddeli Thirupathi Jawahar Sundaram Kheng Cher Yeo Shujahat Ali Khan Pritika Devaraju Sellappan Faculty of Science and Technology Energy and Resources Institute Charles Darwin University Darwin Australia Department of Computer Science and Applications Christ Academy Institute for Advanced Studies Bengluru India Faculty of Science and Technology Charles Darwin University Darwin Australia School of Computing Science and Engineering VIT Bhopal University Bhopal India

ISBN: (数字)9798350383652

ISBN: (纸本)9798350383669

In the current era, digital forensic investigators need specialized tools to extract digital footprints from hard disks. The number of crimes is rising dramatically. This has resulted in an increased number of unresolved cybercrime cases including malicious software, hacking and cyberfraud. Disk forensics or disk investigation is a massive task. It takes weeks to collect the traces of the 1TB hard drive and analyze them. It is a challenging task to detect reliable evidence because of the worldwide use and advancement of digital communication technologies. By using automated tools, only predictable areas of the disk are often investigated, and as a result, latent evidence in the hidden area might be missed. In this paper, we present Chaithu's Framework (CF) that gives us a clear picture of the steps to follow and implement a machine learning algorithm which is able to predict whether the request is related to malware or not by analyzing the large world Microsoft Malware Prediction dataset collected from Kaggle. It consists of a total of 7.8 million data samples with 84 features collected in real time from Windows 7,8,9 and 10 systems. Using predictive machine learning algorithms, frauds can be detected automatically and autonomous actions can be taken to prevent them.

关键词： Analytical models Machine learning algorithms Accuracy Predictive models Malware Real-time systems Software reliability

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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Comparative Study on Evaluating the Performance of Automated Bacterial Colony Counting with Available APP and Software on Generated Image Dataset

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SN Computer Science 2025年第4期6卷 1-16页

作者： Arora, Prachi Tewary, Suman Krishnamurthi, Srinivasan Kumari, Neelam School of Computing Indian Institute of Information Technology Una (IIIT-Una) Una 177209 India Academy of Scientific and Innovative Research (AcSIR) Ghaziabad 201002 India Thin Film Coating Facility CSIR-Central Scientific Instruments Organisation (CSIR-CSIO) Sector 30-C Chandigarh 160030 India Materials Science and Sensor Applications CSIR-Central Scientific Instruments Organisation (CSIR-CSIO) Sector 30-C Chandigarh 160030 India Advanced Materials and Processes Division CSIR-National Metallurgical Laboratory (CSIR-NML) Jamshedpur 831007 India MTCC-Gene bank CSIR-Institute of Microbial Technology (CSIR-IMTECH) Sector 39-A Chandigarh 160039 India

Recent developments in image analysis and interpretation using computer vision techniques have shown potential for novel applications in microbiology laboratories to support the task of automation, aiming for faster and more reliable detection. Image processing techniques and machine learning models can be valuable tools in the screening process, helping technicians spend less time classifying no-growth results and quickly separating the categories for further analysis. In this context, creating a dataset of different bacterial strain images is a fundamental objective for developing and improving the accuracy of image processing models. Therefore, this manuscript acquired a dataset of water samples with different bacterial strain images on a petri dish following a standardized process with controlled conditions of positioning and lighting. The image acquisition device was also developed with a light-emitting diode (LED) and diffuser as a lighting source and a smartphone camera with 16 MP resolution. In addition, the present manuscript also focuses on comparing the accuracy of the proposed algorithm with the available apps and software using the custom-built imaging device. Hence, the resulting dataset consists of 100 images, which is helpful for researchers working in image processing to develop an algorithm for automated counting of bacterial colonies on petri dishes. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bacterial colonies Image datasets Image processing Imaging device Segmentation Water samples

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Improving Energy Efficiency using Colliding Bodies Optimization based Routing Protocol for Wireless Sensor Networks

Improving Energy Efficiency using Colliding Bodies Optimizat...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Prabakaran R. Shalini N Hariharan S. Marieswari Kallakunta Ravi Kumar Maturi Gayatri Lakshmi Apoorva Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai Department of BCA & IT School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Chennai Science and Humanities – EEE R.M.K. College of Engineering and Technology Thiruvallur India Biomedical Engineering P. S. R. Engineering College Department of ECE Koneru Lakshmaiah Education Foundation Vaddeswaram ECE Department Amrita School of Engineering

Wireless sensor network (WSN) contains millions of small, low-power gadgets with a lack of resources are memory and battery power. Such gadgets were deployed in a dispersed manner and were often utilized to monitor and sense applications. Owing to constraints and limited resources of WSNs, routing can be a great difficulty. Routing in WSN was the process of choosing the optimal path for data to travel from a source node to a destination node. The goal of routing in WSN offer dependable and effective transmission while minimizing network overhead and energy consumption. This study designs an Energy Efficient Colliding Bodies Optimization based Routing Protocol (EECBO-RP) for WSN. The end goal of the EECBO-RP algorithm lies in the optimal election of routes to a destination. For achieving reasonable results for the minimization of energy exploitation, the EECBO-RP system derives the fitness function using the following variables: residual energy, distance to BS, and node degree. The EECBO-RP technique chooses the relay nodes so that a way that the overall efficiency of the WSN gets maximized. The simulation values of the EECBO-RP system are tested under several dimensions and the outcomes pointed out the betterment of the EECBO-RP technique.

关键词： Wireless sensor networks Renewable energy sources Voting Routing Minimization Routing protocols Energy efficiency

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The positive mass theorem for asymptotically flat manifolds with isolated conical singularities Dedicated to Professor Weiping Zhang on the Occasion of His 60th Birthday

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Science China Mathematics 2025年第07期 1671-1686页

作者： Xianzhe Dai Yukai Sun Changliang Wang Department of Mathematics University of CaliforniaSanta Barbara Key Laboratory of Pure and Applied Mathematics School of Mathematical SciencesPeking University School of Mathematical Sciences and Institute for Advanced Study Key Laboratory of Intelligent Computing and Applications (Ministry of Education)Tongji University

We prove the positive mass theorem for asymptotically flat(AF for short) manifolds with finitely many isolated conical singularities. We do not impose the spin condition. Instead, we use the conformal blow-up techniqu...

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ROCFL: A Robust Clustered Federated Learning Framework towards Heterogeneous Data

ROCFL: A Robust Clustered Federated Learning Framework towar...

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Intelligent Communication and Networking (ICN), International Conference on

作者： Zihan Li Zhitao Guan Shuai Yuan Ningyu An Xiao Liang School of Control and Computer Engineering North China Electric Power University Beijing China Department of Finance Operations and Information Systems (FOIS) Brock University Canada State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Beijing China

As a decentralized machine learning approach, federated learning has become a key solution for numerous applications. However, one primary challenge lies in the data heterogeneity issue when developing effective federated learning algorithms. While many existing frameworks address the concern of non-independent and identically distributed data(non-IID), they have not considered heterogeneity of data quality. Therefore, we propose ROCFL, a robust clustered federated learning method in this study, which can amplify the disparity in weight allocation between models trained on different quality data. We first develop an optimal clustering matching mechanism that groups clients with similar data distributions. This allows the optimal clustering model to be derived without specifying a predetermined number of clusters. Next, we introduce a personalized weight allocation strategy, which assigns a weight benchmark to each cluster based on its cluster importance index. This strategy mitigates the negative impacts of low-quality data during model aggregation. Finally, we design a federated aggregation strategy grounded in a sampling approach. It not only ensures unbiased sampling but also significantly reduces both computational and communication overheads. Experiments have been carried out and the results demonstrate that higher accuracy and robustness can be achieved by ROCFL in scenarios wherein data distribution and quality heterogeneity coexist, in comparison with the benchmarks.

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CFTD from TQFTD+1 via Holographic Tensor Network, and Precision Discretization of CFT2

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Physical Review X 2024年第4期14卷 041033页

作者： Lin Chen Kaixin Ji Haochen Zhang Ce Shen Ruoshui Wang Xiangdong Zeng Ling-Yan Hung State Key Laboratory of Surface Physics Department of Physics and Center for Field Theory and Particle Physics School of Physics and Optoelectronics Department of Physics International Centre for Theoretical Physics Asia-Pacific Ithaca New York 14853 USA Institute for Advanced Study Institute for Nanoelectronic Devices and Quantum Computing Yau Mathematical Sciences Center Beijing Institute of Mathematical Sciences and Applications Beijing 101408 China

We show that the path integral of conformal field theories in D dimensions (CFTD) can be constructed by solving for eigenstates of a renormalization group (RG) operator following from the Turaev-Viro formulation of a topological field theory (topological quantum field theory) (TQFT) in D+1 dimensions (TQFTD+1), explicitly realizing the holographic sandwich relation between a symmetric theory and a TQFT. Generically, exact eigenstates corresponding to symmetric TQFTD follow from Frobenius algebra in TQFTD+1. For D=2, we construct eigenstates that produce 2D rational CFT path integrals exactly, which curiously connect a continuous-field theoretic path integral with the Turaev-Viro state sum. We also devise and illustrate numerical methods for D=2, 3 to search for CFTD as phase transition points between symmetric TQFTD. Finally, since the RG operator is in fact an exact analytic holographic tensor network, we compute “bulk-boundary” correlators and compare them with the AdS/CFT dictionary at D=2. Promisingly, they are numerically compatible given our accuracy, although further works will be needed to explore the precise connection to the AdS/CFT correspondence.

关键词： Conformal field theory Gauge-gravity dualities Tensor network renormalization Topological field theories Topological order

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Quantum Machine Learning Techniques based on Nurturing Agri-Ontology Framework in Agricultural Science

Quantum Machine Learning Techniques based on Nurturing Agri-...

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Applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： R Deepa V Jayalakshmi P Thilakavathy A Bennet Prabhu R Surendran Department of Computing Technologies School of Computing SRM institute of science and technology Chennai India Department of Computer Applications DRBCCC Hindu College Chennai India Computer Science and Engineering Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India KONE Elevator India Technology & Engineering Centre Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

The absence of a consistent ontological framework often impedes efficient knowledge organization and sharing within the agricultural community, despite the quantity of data created in agricultural research. The lack of defined vocabulary and conceptual frameworks hinders data integration, agricultural research, and innovation by preventing interoperability between databases and systems. To address these issues, this research work proposes the Nurturing Agri-Ontology Framework (NAOF), which offers a systematic approach to creating and updating agricultural ontologies. Ontologies that faithfully portray agricultural ideas and connections can be more easily created with the help of NAOF, which combines ideas from knowledge management, agricultural science, and ontology engineering. NAOF aims to ensure that the ontologies produced reflect the wide range of viewpoints and terminology used in agricultural research by encouraging cooperation among stakeholders, ontology engineers, and domain specialists. In this study, the improvement of knowledge organization, sharing and integration in the agricultural area is shown with the application of NAOF. Using this research study, scientists can build ontologies that improve agricultural decision-making, data interoperability, and the discovery of new information. Using quantum machine learning techniques, a strong foundation of knowledge can be laid for several agricultural applications, such as precision agriculture, crop management, and the formulation of agricultural policies. Ontology coverage, interoperability, scalability, and usability are established evaluation parameters that are used to assess the efficacy of NAOF in this research work. The analysis shows that NAOF effectively solves the problems and creates a better agricultural knowledge ecosystem by thoroughly evaluating it using these standards.

关键词： Technological innovation Systematics Standards organizations Machine learning Organizations Ontologies Stakeholders

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Power Transformer Health State Prediction Based on Multi-attention Coupling Learning

Power Transformer Health State Prediction Based on Multi-att...

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China International Electrical and Energy Conference (CIEEC)

作者： Peng Zhang Jinrui Gan Chunpeng Wu Peng Wu Bo Qi Fei Zhou State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

Transformer is the key apparatus of the power system, and its operational reliability is directly related to the stability of the power grid. Accurately predicting the future health state of transformers can reserve enough time for formulating outage maintenance and power dispatching plans. The existing transformer health state prediction methods are based on the structure of “predict-diagnose”, which is the superposition of two local optimal methods, with error superposition. To fill this gap, a transformer health state prediction method based on multi-attention coupling learning is proposed in this paper. The “situational quantity” integrating the transformer operation state change relationship, the correlation relationship between state quantities, the mapping relationship between the state quantity and the state, the fuzzy relationship between the evaluation result and the actual state, and the traditional expert experience is proposed. Then, the structural optimization attention (SOA) model applicable to “situational quantity” is proposed, and various relationships are treated as individual attention. All kinds of attention are coupled and constrained in the SOA to realize the unification of the prediction process and diagnosis process, obtain the global optimization of prediction and diagnosis, and the problem of error superposition is solved. The actual fault case verification results show that the accuracy of the proposed SOA prediction model can reach 96.33%, which is significantly better than the traditional methods.

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A Short-term Net Load Forecasting Method Based on Two-stage Feature Selection and LightGBM with Hyperparameter Auto-Tuning

A Short-term Net Load Forecasting Method Based on Two-stage ...

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Annual Meeting Industrial and Commercial Power Systems Technical Conference

作者： Shuya Lei Xiao Liang Xiaohui Wang Jingyang Ding Xinxin Ge Fei Wang Jia Feng State Grid Laboratory of Grid Advanced Computing and Applications (State Grid Smart Grid Research Institute Co. Ltd) Beijing China Department of Electrical Engineering North China Electric Power University Baoding China State Grid Zhejiang Electric Power Company Ltd Hangzhou China

Reliable short-term net load forecasting (STNLF) is important for the stable operation of power systems and assists utilities in unit commitment and demand side management. With the increasing penetration of invisible behind-the-meter photovoltaic systems (BTM-PVs) installed close to customers, accurate STNLF is becoming more challenging. The intermittent of PV output is carried into the load due to the coupling of PV output and actual load, which makes the net load complex and difficult to forecast. In addition, the related influencing factors of BTM-PV also have a great impact on load characteristics based on the original features of load demand. To eliminate the redundant features is also one of the difficulties. This paper proposes a machine learning method to improve the accuracy of 24-hourahead net load forecasting, including PV-Load decoupling, feature selection and light gradient boosting machine (LightGBM) with Bayesian optimization. Firstly, the net load is decomposed into PV output and actual load using the data of small amount of observable PV customers. Then a two-step feature selection method is used to select key features for load demand and PV output, respectively, based on the forecasting accuracy of different feature combinations in the validation dataset. The selected features are used to train LightGBM model, and in order to fully maximize the performance of the forecasting model, the parameters of the model are optimized by Bayesian. Compared with other benchmark models, the proposed model has the best forecasting accuracy. The proposed framework is conducted on smart meter dataset in Ausgrid.

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