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Retraction Note: Adaptive rate and modulation scheme for M2M communication using quantum optical and 5G communication

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Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Chillakuru, Prameela Devi Vignesh, G. D. Maheswari, S. Sabeenian, R. S. Mouleswararao, B. Markkandan, S. Artificial Intelligence and Data Science Velammal Institute of Technology Panchetti Ponneri Chennai India Department of ECE St Joseph’s College of Engineering Chennai India Department of ECE Panimalar Engineering College Chennai India Department of ECE Sona College of Technology Salem India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur dt India School of Electronics Engineering (SENSE) Vellore Institute of Technology Chennai India

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Intelligent Kidney Abnormality Detection in CT Images Using GLCM and Neural Networks

Intelligent Kidney Abnormality Detection in CT Images Using ...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Chhavi Bajpai Anitha S Manish Gaur M. Raghini S. Kaliappan Imran Khan Department of Computer Science and Engineering Centre for Advanced Studies AKTU Lucknow India Department of CSE-Cyber Security ACS College of Engineering Bangalore India Department of Computer Science & Engineering Institute of Engineering and Technology AKTU Lucknow India Department of Artificial Intell igence and Data Science Velammal College of Engineering and Technology Madurai India Department of Research and Development Lovely Professional University Jalandhar India Department of Medical Surgical Nursing Sharda School of Nursing Science and Research Sharda University Greater Noida India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Kidney stone illness, one of the most common medical problems worldwide, relieves pain and drives people to the ER. Diagnoses often use imaging, but educated doctors must interpret them. computer-aided diagnostics (CAD) systems help doctors discover kidney stones more accurately. The suggested technique includes preprocessing, feature extraction, and model training. Ultrasound (US) image preprocessing includes restoration, smoothness, sharpness, and contrast enhancement. The Grey Level Co-occurrence Matrices (GLCM) analyze image texture to extract characteristics. Model training uses KELM. The KELM-based model outperforms Extreme Learning Machine (ELM) and K-Nearest Neighbours (KNN) with an average accuracy of 92.54%. This accuracy demonstrates the method can detect kidney stones in ultrasound images. This proposed shows that computer-aided kidney stone detection can be fast and accurate. The combination of GLCM's texture analysis and KELM's classification skills can help medical professionals diagnose kidney stones using imaging.

关键词： Training Solid modeling Accuracy Ultrasonic imaging Computational modeling Medical services Nearest neighbor methods Feature extraction Kidney Medical diagnostic imaging

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Assessing Students for Industry Readiness using Classification Methods 9

Assessing Students for Industry Readiness using Classificati...

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9th ACIS International Virtual Conference on Applied Computing and Information Technology, ACIT 2022

作者： Hossain Molla, Jakir Basak, Sandip Kumar Obaidullah, Sk Md Alam, Parveen Ahmed Goto, Takaaki Sen, Soumya Department of Computer Science and Engineering Aliah University India Department of Data Science Friedrich-Alexander-Universität Germany Department of Management & Business Administration Aliah University India Faculty of Information Sciences and Arts Toyo University Japan A.K.Choudhury School of Information Technology University of Calcutta India

ISBN: (纸本)9781450397605

Although Placement of students is an intrinsic requirement for the students worldwide majorly for professional as well as for higher degree courses. However, often the students are not aware about their skill levels that are considered as the industry readiness parameters. The Institutions often take care of the students to upgrade the skill level to meet the requirement of the Industry. In order to analyze the students based on the several parameters of industry readiness intelligent methods are required to assess them. In this research work, different classification methods are applied on the existing placement data to evaluate whether a student will get a job or not. Accuracies of these methods are compared using a real life data set. This analysis will help the Institutes to take the decision how long the training programs will continue. The result of the classification methods have been improved further using Random oversampling. © 2022 ACM.

关键词： Students

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A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detection

A Dual-Path Framework with Frequency-and-Time Excited Networ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yucong Zhang Juan Liu Yao Tian Haifeng Liu Ming Li School of Computer Science Wuhan University Wuhan China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Duke Kunshan University Kunshan China Oppo Data & AI Engineering System Beijing China University of Science and Technology of China Hefei China

In contrast to human speech, machine-generated sounds of the same type often exhibit consistent frequency characteristics and discernible temporal periodicity. However, leveraging these dual attributes in anomaly detection remains relatively under-explored. In this paper, we propose an automated dual-path framework that learns prominent frequency and temporal patterns for diverse machine types. One pathway uses a novel Frequency-and-Time Excited Network (FTE-Net) to learn the salient features across frequency and time axes of the spectrogram. It incorporates a Frequency-and-Time Chunkwise Encoder (FTC-Encoder) and an excitation network. The other pathway uses a 1D convolutional network for utterance-level spectrum. Experimental results on the DCASE 2023 task 2 dataset show the state-of-the-art performance of our proposed method. Moreover, visualizations of the intermediate feature maps in the excitation network are provided to illustrate the effectiveness of our method.

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Timeliness-Aware Multiple Rumor Sources Estimation in Dynamic Online Social Networks

SSRN

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SSRN 2024年

作者： Huang, Da-Wen Wu, Wenjie Bi, Jichao Li, Junli Gan, Chenquan Zhou, Wei College of Computer Science Sichuan Normal University Chengdu China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating the damage caused by them. However, most studies are not suitable for identifying rumor sources in dynamic OSNs with community structures. Moreover, these studies tend to neglect the impact of timeliness on the spread of rumors, potentially leading to an overestimation of rumors' propagation ability. To overcome these limitations, this paper proposes a multiple rumor sources estimation framework for OSNs. First, a community-based dynamic network model is introduced to depict the temporal nature and community feature of OSNs. Second, a meticulously designed microcosmic SIR model, incorporating the timeliness of rumor topics, is developed to unravel the complex dynamics of rumor propagation. Then, a computationally efficient multiple rumor sources estimation algorithm is proposed. This algorithm utilizes the infection information collected by sensors and applies the maximum likelihood estimation (MLE) method to identify the rumor sources. Finally, experimental results on real-world and synthetic temporal networks demonstrate the effectiveness of the proposed rumor source estimation algorithm. © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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Generative adversarial networks enhanced location privacy in 5G networks

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Science China(Information Sciences) 2020年第12期63卷 41-52页

作者： Youyang QU Jingwen ZHANG Ruidong LI Xiaoning ZHANG Xuemeng ZHAI Shui YU School of Information Technology Deakin University School of Computer Science University of Technology Sydney National Institute of Information and Communications Technology (NICT) School of Information and Communication Engineering University of Electronic Science and Technology of China Center of AI and Big Data Southeast Digital Economic Development Institute

5G networks, as the up-to-date communication platforms, are experiencing fast booming. Meanwhile, increasing volumes of sensitive data, especially location information, are being generated and shared using 5G networks for various purposes ceaselessly. Location and trajectory information in the published data has always been and will keep courting risks and attacks by malicious adversaries. Therefore, there are still privacy leakage threats by simply sharing the original data, especially data with location information, due to the short cover range of 5G signal tower. To better address these issues, we proposed a generative adversarial networks(GAN) enhanced location privacy protection model to cloak the location and even trajectory information. We use posterior sampling to generate a subset of data, which is proved complying with differential privacy requirements from the end device side. After that, a data augmentation algorithm modified from classic GAN is devised to generate a series of privacy-preserving full-sized synthetic data from the central server side. With the synthetic data generated from a real-world dataset, we demonstrate the superiority of the proposed model in terms of location privacy protection, data utility, and prediction accuracy.

关键词： 5G privacy preservation generative adversarial nets differential privacy

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Optimizing Blockchain Mining Decisions Using Deep Reinforcement Learning Algorithms

Optimizing Blockchain Mining Decisions Using Deep Reinforcem...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Anne Sreyas Venkatesam Karnati Shainy Reddy Shanmugasundaram Hariharan Bibhuti Bhsan Dash Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar Odisha India Department of CSE Sreenivasa Institute of Technology and Management Studies Chittoor Anthra Pradesh India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Blockchain technology is a distributed database that records the transactions across a network of computers. Blockchain technology ensures security and transparency of the data. Blockchain mining refers to the process of verifying the transactions in a blockchain network. However, blockchain mining consumes high energy and solving complex mathematical problems and puzzles. We this study addressed these challenges of blockchain mining using Reinforcement Learning (RL). We have developed a custom environment, BlockchainMiningEnv using openAIGym framework. This environment specifies key factors of blockchain mining like difficulty level and energy cost. These are used to create a realistic model for training an intelligent agent. The agent is trained with algorithms like proximal policy optimizing (PPO) Soft Actor-Critic (SAC) and Advantage Actor-Critic(A2C) to learn how to deal with these computational resources. The main aim of the agent is to maximize rewards by increasing mining efficiency and minimizing the energy consumption. In this paper we have combined blockchain technology with Reinforcement Learning (RL) to improve efficiency and sustainability of blockchain mining by maintaining the security and transparency of the data.

关键词： Training Scalability Reinforcement learning Robustness Mathematical models Blockchains data mining Security Sustainable development Tuning

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Multikernel:Operating system solution to generalized functional safety

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Security and Safety 2023年第2期2卷 30-43页

作者： Yijing Song Huasheng Dai Jinhu Jiang Weihua Zhang School of Computer Science Fudan UniversityShanghai 200433China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Institute of Big Data Fudan UniversityShanghai 200433China Parallel Processing Institute Fudan UniversityShanghai 200433China

With the trend of digitalization,intelligence,and networking sweeping the world,functional safety and cyber security are increasingly intertwined and overlapped,evolving into the issue of generalized functional *** system reliability technology and network defense technology cannot provide quantifiable design implementation theories and *** the cornerstone of software systems,operating systems in particular are in need of efficient safety *** DHR architecture is a mature and comprehensive solution,and it is necessary to implement an OS-level DHR architecture,for which the multi-kernel operating system is a good *** multi-kernel operating system takes the kernel as the processing scenario element and constructs redundancy,heterogeneity,and dynamism on the kernel,so it has the generalized robustness of the DHR *** article analyzes the significance and requirements of OS-level DHR architecture,and systematically explains how the multi-kernel operating system responds to the requirements of OS-level DHR architecture by analyzing the technical routes of multi-kernel operating systems and develops an operating system solution idea for the generalized functionally safety.

关键词： Metaverse New infrastructure Security base Highly trusted network Trusted identiry Trusted circulation of data

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Are Deep Point Cloud Classifiers Suffer From Out-of-distribution Overconfidence Issue?

Are Deep Point Cloud Classifiers Suffer From Out-of-distribu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xu He Keke Tang Yawen Shi Yin Li Weilong Peng Peican Zhu Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China School of Data Science Fudan University Shanghai China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Artificial Intelligence Optics and Electronics (iOPEN) Northwestern Polytechnical University Xi'an China

3D point cloud perception using deep neural networks (DNNs) has been a trend for various application scenarios. However, the black-box nature of DNNs will bring many hidden risks as in the 2D image field. In this paper, we present a preliminary evaluation on the out-of-distribution (OOD) overconfidence issue of deep point cloud classifiers, which has been proven to exist in deep 2D image classifiers, i.e., OOD inputs will lead to overconfident predictions on predefined categories. We also investigate whether a simple thresholding baseline and two modern OOD detection solutions can handle the issue by detecting OOD samples. Extensive experiments with four representative deep point cloud classifiers train/evaluate on different in/out-of-distribution point clouds validate the severity and knottiness of the OOD overconfidence issue. Our investigation will provide the groundwork for future studies on handling the OOD overconfidence issue of DNN classifiers for 3D point clouds.

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CosineRecom: A KNN-Based Movie Recommendation System Using Cosine Similarity

CosineRecom: A KNN-Based Movie Recommendation System Using C...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Mandeep Singh Prafull Mishra Nitin Aggarwal Vinay Kumar Pramod Kumar Sharma Ritesh Yadav School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India Supervisor Oracle Development Database Admin Data Science MFPS PA USA Department of CSIT KIET Group of Institutions Ghaziabad UP India Department of Applied Science KIET Group of Institute Ghaziabad UP India Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Information overload is a major problem for many people who use the internet. This is because there is so much information available that it can be hard to know where to start. Filtering tools, like the Recommender System, are needed to solve this problem. These tools give users access to relevant information and let them customise their search results based on their liking. The Collaborative Filtering Recommendation System finds the group of active users who are your closest neighbours. This study shows many ways to use the supervised machine learning algorithm (KNN) method for the Movie recommendation System with different similarity measures, such as the cosine metric, Squared Error Similarity, Linear Correlation Coefficient, and Baseline Predictor Correlation. For the movie recommender system, these many changes to the KNN algorithm were tested on real data from the given dataset and compared based on accuracy measures such as the number of matching pairs and different types of error like mean absolute error, squared mean error, and squared root mean error. By making changes to the web browser, this recommendation system processing tool could be used as a substitute in the real world.

关键词： Machine learning algorithms Filtering Collaborative filtering Measurement uncertainty Machine learning Nearest neighbor methods Motion pictures Prediction algorithms Matrices Recommender systems

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