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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Developing Campus Digital Twin with Integrated 3D Point Clouds and 3D Modeling Techniques

Developing Campus Digital Twin with Integrated 3D Point Clou...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Ryan Nathan Utama Jonathan Sutjiatmadja Fabrian Osmond Lucas Stefan Alexander A S Gunawan Andry Chowanda Edy Irwansyah Fabian Surya Pramudya Game Application & Technology Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Exploring digital twin technology through the creation of 3D digital model offers a powerful tool for enhancing real-time monitoring, predictive analysis, and optimization of complex systems. The purpose of this study is to develop a 3D digital model of our building campus as the first step towards developing a digital twin of the university. In this study, we develop a digital model with integrated 3D point clouds and 3D modeling techniques. By scanning the physical model of the campus, data in the form of point clouds is extracted and unnecessary noise is deleted using Autodesk Recap Pro. These point clouds are used as a benchmark to ensure the 3D models created using Blender are as accurate as possible to the physical model. After successfully creating the digital model, several features/interactions are implemented using Unity to mimic the physical model, thus developing the digital twin. The digital twin of the campus will be used to develop a virtual tour of the campus. We concluded that the 3D digital model of the campus was successfully developed, despite the time-consuming nature of the process. We hope that this study serves as a reference to optimize and enhance digital twin development in the future.

关键词： Point cloud compression Solid modeling Three-dimensional displays Buildings Benchmark testing Feature extraction Data models Real-time systems Digital twins Data mining

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Comparison of Machine Learning Techniques Based on Feature Reduction to Detect Diabetic Retinopathy 9

Comparison of Machine Learning Techniques Based on Feature R...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Singh, Devendra Joshi, Deepika Dobhal, Dinesh C. Pant, Janmejay Pant, Himanshu Graphic Era Hill University Computer Science and Engineering Department Nainital India Amrapali Group of Institutes Faculty of Technology and Computer Application Haldwani India Graphic Era Deemed to Be University Computer Application Department Uttarakhad Dehradun India Graphic Era Hill University School of Computing Department Nainital India

ISBN: (纸本)9798350397376

Diabetic retinopathy is considered as an eye disorder condition that can affect persons who are diagnosed positive against diabetes. This condition causes damage to the retina's blood vessels. Diabetic retinopathy detection at an early stage may help patients protect their vision. This disease is often asymptomatic in the early stages but has a significant impact in the latter stages. The timely identification and intervention of diabetic retinopathy (DR) can greatly mitigate the potential for visual impairment. It takes time, effort, and money for ophthalmologists to manually diagnose DR retina fundus images. So there is a need for a screening system that can detect the disease in its early stages. There are various machine learning-based approaches available to detect diabetic retinopathy. In our proposed work first, we have to select the optimal features then we perform the comparative analysis with the help of various machine learning algorithms. We have used Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machine (SVM), Naïve Bayes, and AdaBoost. In our experiment, we observed that Logistic Regression achieved the highest accuracy of 74.2 with optimal features. © 2023 IEEE.

关键词： Adaptive boosting

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Efficient opportunistic routing with social context awareness for distributed mobile social networks

Efficient opportunistic routing with social context awarenes...

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作者： Xu, Fang Xiao, Nan Deng, Min Xie, Yong Xiong, Zenggang Xu, Qiong School of Computer and Information Science Hubei Engineering University Xiaogan China Department of Computer Technology and Application Qinghai University Xining China

mobile social networks (MSNs) are developed from mobile ad hoc networks. Nodes in such networks usually have social characteristics. In recent years, researchers are trying to use the social characteristics of the network to propose new data forwarding metrics, so as to design more efficient routing algorithms. However, most of the proposed algorithms only consider local context information, which leads to the performance of the routing is not optimized enough. In this paper, we introduce two key metrics, namely, social relationship and social activity. The metrics will be used to search the best data forwarding nodes to improve the probability of data delivery. We propose a prediction-based social-aware opportunistic routing (PSOR). In the proposed method, node's social profiles are used to search relay candidates set, and the discrete-time semi-Markov prediction model is used to find the probability distribution of node transition between communities. Many simulation experiments based on real traces show that the proposed PSOR algorithm is more efficient to maximize the packet delivery probability than other state-of-the-art algorithms. © 2019 John Wiley & Sons, Ltd.

关键词： Forecasting

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Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach 8

Determining Variables Associated with Annual Oil Palm Yield:...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Elwirehardja, Gregorius Natanael Suparyanto, Teddy Miftakhurrokhmat Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta55282 Indonesia Faculty of Industrial Technology Universitas Islam Indonesia Yogyakarta55584 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 RRʹ. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed. © 2023 The Authors. Published by Elsevier B.V.

关键词： Machine Learning Oil Palm Yield SHAP XAI XGBoost

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Enhancing Rule-Based Methods for Pulmonary Effusion Diagnosis 15

Enhancing Rule-Based Methods for Pulmonary Effusion Diagnosi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kharbas, Vikas Kumar Raghu, N. Choudhury, Priyabati Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India Arka Jain University Department of Pharmacy Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

The diagnosis of pulmonary effusion is a difficult assignment for scientific practitioners because of the excessive variance in the presentation and manifestation of the circumstance. Maximum current diagnosis structures rely upon the use of either sensory/visible remark of chest radiographs (CRs) by radiologists or handcrafting statistical features through area experts with the use of computer-aided analysis (CAD) tools. However, those methods regularly need better generalization and are constrained via their reliance on conventional heuristic algorithms, as well as the mistake-inclined technique of guide identification of features. This paper explores the capacity of the usage of rule-based total methods as better diagnostic structures for pulmonary effusion. Particularly, a rule-based total device primarily based on supervised mastering-based total fashions is proposed as an alternative to traditional feature identification and model development strategies. The proposed device includes an output layer and an enter layer, which includes photograph features and radiological descriptors. Experiments have been conducted on the LIDC-IDRI dataset, and the proposed system outperformed traditional feature-based approaches in terms of both accuracy and specificity. Furthermore, the use of rule-based methods for diagnosis of pulmonary effusion provides a more reliable diagnosis process, which may reduce the radiological review times and improve the accuracy of non-radiological diagnosis. © 2024 IEEE.

关键词： Conducted Diagnosis Effusion Identification Manifestation Pulmonary Radiographs

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Evaluating the Impact of Different Features on the Efficiency of Emotion Recognition Algorithms in Text 2

Evaluating the Impact of Different Features on the Efficienc...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Sharma, Sunil Ezhilarasan, G. Ghosh, Rajkumari Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Electrical and Electronics Engineering Karnataka Bangalore India ARKA JAIN University Department of English Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

The effectiveness today's emotion recognition algorithms in texts has become increasingly more critical in current years today's its programs in supporting users gauge actual-time reactions from text-based totally conversations. On this paper, we advise a fixed state-of-the-art experiments to assess the effect that different functions, along with syntactic and semantic functions, have at the efficiency contemporary emotion reputation algorithms in textual content. We use a manually annotated dataset from the automatic have an effect on recognition in text (AART) task to create characteristic units consultant of various ranges latest functions. Those feature units are then used to assess the overall performance latest emotion recognition algorithms - Recursive Neural Networks and Convolutional Neural Networks. The results display that incorporating syntactic and semantic functions can improve the accuracy present day emotion popularity algorithms at a charge ultra-modern 6-10% while as compared with the baseline overall performance. From the findings modern this test, we conclude that incorporating syntactic and semantic features can drastically enhance the overall performance trendy emotion popularity algorithms in text-based totally conversations. © 2024 IEEE.

关键词： Semantics

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KVSAgg: Secure Aggregation of Distributed Key-Value Sets 39

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Wu, Yuhan Dong, Siyuan Zhou, Yi Zhao, Yikai Fu, Fangcheng Yang, Tong Niu, Chaoyue Wu, Fan Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350322279

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users' privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem - key-value set secure aggregation - that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously. © 2023 IEEE.

关键词： Vectors

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Interference coordination techniques for D2D communications in a spectrum sharing environment 2

Interference coordination techniques for D2D communications ...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Inzimam Ranganathaswamy, M.K. Kumar, Ashwini Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India ARKA JAIN University Department of Mechnical Engineering Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

device-to-tool (D2D) communications has become an essential a part of allowing understanding transfer, records sharing, and collaboration. As such, green usage of spectrum assets for D2D communications in a spectrum-sharing surroundings is important for making sure equity and advanced consumer revel in. This paper proposes diverse interference coordination techniques for D2D communications in a spectrum sharing environment. These strategies help in enhancing Qu's by means of mitigating interference inside the goal region, while ensuring minimal disruption to the existing spectrum allocations. The proposed strategies include: Node Clustering and Grid Partitioning;Adaptive carrier Sensing and Dynamic Channel Allocation;Interference Cancellation and signal Combining;and more than one access techniques. The usage of simulation results, the efficacy of the proposed techniques become evaluated under various degrees of interference in the environment. Consequences display that these strategies drastically improve overall performance for a wide variety of applications, together with streaming films, voice communique, and information transfer. © 2024 IEEE.

关键词： Communications Environment Essential Interference Surroundings Understanding

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