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International Conference on computer and Communications (ICCC)

作者： Ming Li Peiyan Yuan College of Computer and Infomation Engineering Henan Normal University Xinxiang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Lyapunov optimization is a promising technology for stabilizing systems and reducing energy consumption in multi-edge-server collaborative computing scenarios. Existing studies have led to the instantaneous greedy issue, owing to the constant operation in the queue length calculation and local optimization. Therefore, this study tries to improve the Lyapunov optimization with deep reinforcement learning (DRL). First, the Lyapunov optimization framework was employed to build the queue model and derive the upper bound of the system objective function. Second, the system action and state space were constructed based on the queue model, and a Lyapunov optimization module was formulated to minimize the system energy consumption. Third, a reward function was created by integrating the reward discount factor. Finally, a soft actor-critic algorithm was used to train the adaptive cloud-edge service offloading strategy. Many experimental results show that the system does not need to obtain a local optimal solution in each time slot to ensure the stability of the queue and reduce energy consumption.

关键词： Energy consumption Upper bound Heuristic algorithms Collaboration Deep reinforcement learning Linear programming Stability analysis User experience Real-time systems Optimization

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A Lyapunov Optimization Strategy with Deep Reinforcement Learning in Cloud-Edge Collaborative Service Offloading Scenarios 10

A Lyapunov Optimization Strategy with Deep Reinforcement Lea...

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10th International Conference on computer and Communications, ICCC 2024

作者： Li, Ming Yuan, Peiyan College of Computer and Infomation Engineering Henan Normal University Xinxiang China

关键词： Reinforcement learning

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer Systems Science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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CRipS: Coconut Ripeness Stage Detection System

CRipS: Coconut Ripeness Stage Detection System

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Venkatesh Prajwal, K.P. Patil, Preeti Priyanka, G. Poojary, Prajwal Basapur, Satish B University of Visvesvaraya College of Engineering Dept. of Computer Science and Engg. Bangalore India Dr. Ambedkar Institute of Technology Dept. of Infomation Science and Engg. Bangalore India

ISBN: (纸本)9798350300826

Manual Coconut harvesting requires tree-climbing skill, it is hazardous because of the tree's hard shell, height and high occlusions on the tree. Further, it is difficult to determine whether a coconut is ripe or not because of the outside illumination and various background. The development of a coconut ripen stages detection system with the help of deep learning techniques is of great interest to farmers. This research work proposes a deep learning-based instance segmentation technique to identify different coconut ripeness stages. To improve instance segmentation accuracy to identify different coconut ripeness stages, the SOLOv2 model is used. In this research work, collected image dataset consisting of approximately 500 RGB images of different types of coconut fruits in coconut plantations during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed SOLOv2 model was trained, validated, and tested on 500 manually acquired and augmented images. On the test dataset, the mean average precision (mAP) and Average Precision (at different intersections-over-union (IoU) thresholds), Average Precision (at different intersection-over-union (IoU) thresholds) attained by the model were 0.628, 0.628 and 0.811, respectively, with an average precision and recall for identifying ripeness stages as 0.912 and 0.883, respectively. I data enables the creation of a comprehensive vision system to pinpoint the cutting spot on the coconut cluster and select the harvesting method. The experimental results enable us to develop an automated ripeness detection system to determine the ripeness stages for harvesting and pinpointing the cutting spot on the coconut tree. © 2023 IEEE.

关键词： Harvesting

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Deepfake Detection Using Adversarial Neural Network

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computer Modeling in engineering & Sciences 2025年第5期143卷 1575-1594页

作者： Priyadharsini Selvaraj Senthil Kumar Jagatheesaperumal Karthiga Marimuthu Oviya Saravanan Bader Fahad Alkhamees Mohammad Mehedi Hassan Department of Computer Science and Engineering Mepco Schlenk Engineering CollegeSivakasi626005India Department of Electronics and Communication Engineering Mepco Schlenk Engineering CllegeSivakasi626005India Department of Infomation Systems Colege f Computer and Information SciencesKing Saud UniversityRiyadhIl45Saud Arabia

With expeditious advancements in AI-driven facial manipulation techniques,particularly deepfake technology,there is growing concern over its potential *** pose a significant threat to society,partic-ularly by infringing on individuals’*** significant endeavors to fabricate systems for identifying deepfake fabrications,existing methodologies often face hurdles in adjusting to innovative forgery techniques and demonstrate increased vulnerability to image and video clarity variations,thereby hindering their broad applicability to images and videos produced by unfamiliar *** this manuscript,we endorse resilient training tactics to amplify generalization *** adversarial training,models are trained using deliberately crafted samples to deceive classification systems,thereby significantly enhancing their generalization *** response to this challenge,we propose an innovative hybrid adversarial training framework integrating Virtual Adversarial Training(VAT)with Two-Generated Blurred Adversarial *** combined framework bolsters the model’s resilience in detecting deepfakes made using unfamiliar deep learning *** such adversarial training,models are prompted to acquire more versatile *** experimental studies,we demonstrate that our model achieves higher accuracy than existing models.

关键词： Deepfake generalization forgery detection pixel-wise Gaussian blurring virtual adversarial training

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CRipS: Coconut Ripeness Stage Detection System

CRipS: Coconut Ripeness Stage Detection System

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Venkatesh Prajwal K P Preeti Patil Priyanka G Prajwal Poojary Satish B Basapur Dept. of Computer Science and Engg. University of Visvesvaraya College of Engineering Bangalore India Dept. of Infomation Science and Engg. Dr. Ambedkar Institute of Technology Bangalore India

关键词：

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Using Machine Learning Techniques for Cancer Classification

Using Machine Learning Techniques for Cancer Classification

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Reddy, Ch. Srinivasa Singh, Rajesh Bhavani, R. Dasgupta, Sanhita Singh, Yashpal Pratan Singh, Suryabhan Vigan's Institute of Information Technology Department of IT Duvvada India Uttaranchal University Uttaranchal Institute of Technology India Saveetha Institute of Medical and Technical Sciences Institute of Computer Science and Engineering Saveetha School of Engineering Chennai600124 India Bir Bikram Department of Computer Memorial College Tripura Agartala India Jain Deemed to Be University Department of Computer Science & Engineering Karnataka Bengaluru India Deen Dayal Upadhyaya Gorakhpur University Institute of Engineering and Technology Department of Infomation Technology Gorakhpur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Given the extremely high death and suffering rates caused by breast cancer, women are particularly at risk. Because of this, it is imperative that algorithms be developed to find breast disease's early warning signs. Decision trees, random forests, regression analysis, and Mri are four categorised algorithms that detect cancer, and the end results were contrasted. Machine learning is essential for predicting breast cancer's early signs. Three data augmentation different classifiers will be used in this research. when categorising algorithms Data with unbalanced classifications are a serious issue that calls for careful treatment and preprocessing. We'll train the various M.L. models using a large dataset of breast cancer patients. The optimal method for this issue is ultimately determined by comparing all the strategies in terms of efficiency and precision. Our research will demonstrate the efficacy of several methods for this category of breast cancer so that we can use the proper strategy. © 2022 IEEE.

关键词： Convolutional neural networks

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Dual-Functional Radar-Communication Based on Frequency Modulated Continuous Wave Exploiting Constraint Frequency Hopping

SSRN

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SSRN 2023年

作者： Xu, Rui Wen, Ruiming Gang, Li Chu, Chu Wen, Guangjun School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China School of Infomation and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China College of Computer Science Sichuan Normal University Sichuan Chengdu610101 China

This paper proposes a dual-functional radar- communication (DFRC) scheme based on the constrained frequency hopping (C-FH) chirp waveform. For communication function, the FH sequence mapping and up/down chirp are used to embed the data. We give explicit rules for traversal-FH sequence to adjust the number of configurable frequency points for each chip, and optimize the communication symbol error rate (SER) at the receiver. For radar signal processing, after coherent processing and sampling, we proceed time-domain extension and range-bin phase compensation (TE-RBPC) according to the FH value, achieving the same range profile as unmodulated frequency modulated continuous wave without influence from different FH sequences. We analyze the deterioration effects of non-traversal FH sequences, to verify the significance of traversal rule in TE-RBPC for C-FH-chirp waveform. The simulation results show that the proposed CFH-chirp waveform reduces out-of-band power leakage by 10 dB compared with phase shift keying scheme and provides 5 dB improvement of signal noise ratio under the same SER compared with unconstrained FH-chirp scheme. Compared with the scheme without compensation, the main lobe width of range profile in TE-RBPC scheme is reduced to 1/16 and the sidelobe amplitude is reduced by about 39 dB. © 2023, The Authors. All rights reserved.

关键词： Frequency hopping

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TransRegex: Multi-modal Regular Expression Synthesis by Generate-and-Repair

TransRegex: Multi-modal Regular Expression Synthesis by Gene...

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International Conference on Software engineering (ICSE)

作者： Yeting Li Shuaimin Li Zhiwu Xu Jialun Cao Zixuan Chen Yun Hu Haiming Chen Shing-Chi Cheung State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Hong Kong University of Science and Technology Hong Kong China Science #x0026 Technology on Integrated Infomation System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665402965;9781665448314

Since regular expressions (abbrev. regexes) are difficult to understand and compose, automatically generating regexes has been an important research problem. This paper introduces TransRegex, for automatically constructing regexes from both natural language descriptions and examples. To the best of our knowledge, TransRegex is the first to treat the NLP-and-example-based regex synthesis problem as the problem of NLP-based synthesis with regex repair. For this purpose, we present novel algorithms for both NLP-based synthesis and regex repair. We evaluate TransRegex with ten relevant state-of-the-art tools on three publicly available datasets. The evaluation results demonstrate that the accuracy of our TransRegex is 17.4%, 35.8% and 38.9% higher than that of NLP-based approaches on the three datasets, respectively. Furthermore, TransRegex can achieve higher accuracy than the state-of-the-art multi-modal techniques with 10% to 30% higher accuracy on all three datasets. The evaluation results also indicate TransRegex utilizing natural language and examples in a more effective way.

关键词： Natural languages Maintenance engineering Tools Software engineering

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TRANSREGEX: Multi-modal regular expression synthesis by generate-and-repair

arXiv

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arXiv 2020年

作者： Li, Yeting Li, Shuaimin Xu, Zhiwu Cao, Jialun Chen, Zixuan Hu, Yun Chen, Haiming Cheung, Shing-Chi State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Hong Kong University of Science and Technology Hong Kong Science & Technology on Integrated Infomation System Laboratory Institute of Software Chinese Academy of Sciences Beijing China

Since regular expressions (abbrev. regexes) are difficult to understand and compose, automatically generating regexes has been an important research problem. This paper introduces TRANSREGEX, for automatically constructing regexes from both natural language descriptions and examples. To the best of our knowledge, TRANSREGEX is the first to treat the NLP-and-example-based regex synthesis problem as the problem of NLP-based synthesis with regex repair. For this purpose, we present novel algorithms for both NLP-based synthesis and regex repair. We evaluate TRANSREGEX with ten relevant state-of-the-art tools on three publicly available datasets. The evaluation results demonstrate that the accuracy of our TRANSREGEX is 17.4%, 35.8% and 38.9% higher than that of NLP-based approaches on the three datasets, respectively. Furthermore, TRANSREGEX can achieve higher accuracy than the state-of-the-art multi-modal techniques with 10% to 30% higher accuracy on all three datasets. The evaluation results also indicate TRANSREGEX utilizing natural language and examples in a more effective way. © 2020, CC BY.

关键词： Natural language processing systems

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