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13th International Advanced Computing Conference, IACC 2023

作者： Singh, Pushpendra Tyagi, Chandra Shekher Sharma, Davesh Kumar Singh, Mahesh Kumar Choudhary, Pushpa Singh, Arun Kumar Department of Computer Science and Engineering SRMIST Delhi-NCR Campus Modinagar UP Ghaziabad India Department of Computer Science and Engineering Dronacharya Group of Institutions UP Greater Noida India School of Computer Science and Engineering Galgotias University UP Greater Noida India Computer Science and Engineering Greater Noida Institute of Technology UP Greater Noida India

ISBN: (纸本)9783031567025

The term "heart or cardiovascular disease" is frequently used to refer to a variety of heart-related issues. It is one of the illnesses with the highest mortality rate. Its fatality rate results in nearly 17 million deaths worldwide. Given the daily increase in heart disease cases, predicting any disease early is critical and concerning. Early detection of heart disease is a difficult task that must be accomplished carefully and effectively by gathering precise information about the patient who is at risk for heart disease. To predict whether a person has cardiac disease, there are numerous AI-based (Machine/Deep learning) Models available considering medical characteristics (features) of heart disease. By comparing and contrasting several machine learning classifiers, this paper proposed a heart disease prediction system. This system will aid in identifying and predicting whether the patient is likely to have or be diagnosed with heart disease based on his or her current medical state. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Proactive Congestion Management in Next-Generation Wireless Networks: Evaluating Traditional Approaches Against Machine Learning Models 2

Proactive Congestion Management in Next-Generation Wireless ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Thangaraj, Johnjustin Mohanraj, T. Caleb, S. Srija, J. Latha, R. Rajendran, Pushpalatha Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Coimbatore Department of Computer Science Engineering India Karunya University Karunya Institute of Technology and Sciences Department of Computer Science and Engineering India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communications Engineering Chennai India

ISBN: (纸本)9798350364699

sensible congestion control approaches continue to be imperative in attaining the highest capability of the next-generation facilities for wireless networks. To this end, the objective of the present research is to establish the effectiveness of the study conducted with classical congestion management techniques to that of the machine models. The proposed work environments a novel and controlled setup of the next-generation wireless network and applies, assesses, and optimizes the traditional QoS and traffic-shaping methodologies, and generic AI forms of the supervised learning and reinforcement learning. Other standards that were utilized and incorporated in the efficient aspect included the latency, through put and packet loss with the view of evaluating efficiency levels of the employed methods. This simply measures that the use of ML is more superior to the conventional methods because it relieves latency and packet loss while at times enhancing through put. The analysis of the two states' comparison proof that congestion recovery problems are more fit for the application of ML algorithms in modern transport systems due to better flexibility and online decision-making chances. Nevertheless, it discusses some problems that exist and they include;The major challenge of how to integrate the ML model in the system and the need to obtain massive training data. Therefore, one can state that the approach of applying more preventive ML models in controlling congestion also appears to be wiser when it comes to deploying lesions over wireless networks from the viewpoints of efficiency and scalability. Future work includes: working with real data;further work on the Advanced ML section;Security and Compliance. © 2024 IEEE.

关键词： Efficiency

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Image Denoising Framework Employing Auto Encoders for Image Reconstruction 1

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Jain, Shruti Bharti, Monika Jindal, Himanshu Department of Electronics and Communications Engineering Jaypee University of Information Technology Himachal Pradesh Waknaghat Solan173234 India Department of Computer Science Engineering ASET Amity University Punjab Punjab Mohali140603 India

ISBN: (数字)9789819986460

ISBN: (纸本)9789819986453

Auto Encoder (AE) can be used in denoising of images. It is a type of neural network that can reconstruct the input. An auto-encoder is to represent a (sparse) input dataset in a compressed form that retains the most relevant information such that it may be reconstructed at the output with minimal loss from the compressed representation. In this paper, deep AE, denoising AE, and variational AE are used. Any AE where an extra constraint is put on the bottleneck to have a low KL divergence from a Normal Distribution is a Variational AE. There are multiple ways in which Variational AE is used, but the most common one is generative. The decoders on the top of the bottleneck can be used to generate new data points. Maximum accuracy of 88.85% is observed using the Denoising autoencoder while 76.25% and 81.44% are observed for deep and Variational autoencoder, respectively. 8.3% and 14.18% accuracy improvement is observed in Variational and deep AE, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image reconstruction

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Video Summarization using Speech Recognition and Text Summarization

Video Summarization using Speech Recognition and Text Summar...

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Emerging technology (INCET), International Conference for

作者： Tirath Tyagi Lakshaya Dhari Yash Nigam Renuka Nagpal Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh India

Videos on the internet have been increasingly becoming the chief source of knowledge and information in today’s digital age. However, with increasing length of videos and diminishing time to spare in everyone’s lives, a need has emerged for Video Summarization tools that can provide a good summary about the content of videos without the need to watch videos in their entirety. In this paper, we introduce a two-fold approach to fetch the subject matter of videos through effective summarization. The employed approach comprises of two phases: the first phase involves performing speech-to-text conversion using an Automatic Speech Recognition(ASR) system based on a Convolutional Neural Network(CNN) for generating respective transcripts for input videos while the second phase involves performing Extractive Text Summarization to summarize the text generated by extracting the important information.

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Design and research on automatic insertion and bending wire device for power socket and soft wire connections 4

Design and research on automatic insertion and bending wire ...

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4th International Conference on Mechanical engineering, Intelligent Manufacturing, and Automation technology, MEMAT 2023

作者： Gao, Zan Yang, Rui Fan, Yong Xie, Wu Zhang, Huimin Guilin University of Electronic Technology School of Mechanical and Electrical Engineering Guilin541214 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541214 China Guangxi Normal University School of Computer Science and Information Engineering Guilin541004 China

ISBN: (纸本)9781510674820

To solve the problem of low efficiency involved in manually inserting power sockets into soft wires, a new system called an automatic insertion wire device of mechanical arm as a part of intelligent agents has been designed. It has a manipulator end lead channel actuator, a two-degree-of-freedom manipulator, a scissor bending mechanism and a control system. A coordinate system has been established for kinematics analyses and inverse kinematics solution. The joint angles of the manipulator when reaching the terminal hole of the power socket are 47.7° and 91.3° for joint angles 1 and 2, respectively. The motions of the scissor bending mechanism are analyzed using a coordinate system, and the rotation angle of support rod 1 is found to be 32.3° after the completion of soft wire bending. A model of the actuator and scissor bending mechanism is created using SolidWorks, and motion simulations are carried out to verify the insertion of soft wire. A prototype is built and tested, which demonstrates that the angle variation range of the soft wire head part due to push block movement is between 58° and 63°, while the angle variation range of the shear fork bending mechanism to other parts of the soft wire is between 168° and 173°. All parameters meet the anticipated goal, effectively resolving the problem of low efficiency. © 2024 SPIE.

关键词： Wire

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Network Abnormal Traffic Detection Approach Based on Multi-Scaled Deep-CapsNet 5

Network Abnormal Traffic Detection Approach Based on Multi-S...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Tanwar, Shashi Thokala, Rishi Reddy Thapak, Sonika Maranan, Ramya Kumar, Sudheer Sravanthi, S. Aravali College of Engineering and Management Department of Computer Science and Engineering Haryana Faridabad India Srm Institute of Science and Technology Computer Science Engineering India Ies Collage of Technology Computer Science and Engineering India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Piet Parul University Computer Science & Engineering Gujrat Vadodara India St. Martin's Engineering College Department of Computer Science Engineering secunderabad India

ISBN: (纸本)9798350321425

It is crucial for network security to be able to detect a variety of threats and unusual network traffic. In order to accomplish aberrant traffic detection, existing detection methods required enormous volumes of data. However, there are rare circumstances in which even a fewshot attack sample can be stopped. The shift in feature attitude will also have an impact on traffic attribute discrimination. However, the standard neural network model is unable to recognize this shift in perspective. As a result, few-shot sample anomalous traffic detection has poor accuracy and efficiency. This research has proposed a strategy for detecting unusual network traffic with a small number of samples. Multi-scale Deep-CapsNet and adversarial-reconstruction (MDAR) made up the system. To begin, the Deep-CapsNet's EM vector clustering is revamped. The prediction is finalized by transforming low-level to high-level features using the attitude transformation matrix. Second, the Deep-CapsNet was improved with the help of a multi-scale convolutional capsule. Finally, a reconstruction classification network (ARCN) that is vulnerable to adversarial input is proposed. Classification of the supervised source data and reconstruction of the unsupervised target data are both completed. In addition, an adversarial training technique that reduced the impact of noise on the reconstruction process is suggested. Using a mixture of MDAR, classification is obtained from a small number of samples (point 4). The results are tested on the ICSX2012 and CICIDS2017 datasets. The experimental outcomes validate the superior training performance of the approach. Additionally, its accuracy is unparalleled in both two- and multi-classification tasks. It is especially useful for real-time few-shot aberrant network traffic detection because to its good anti-noise performance and quick operating time. © 2023 IEEE.

关键词： Network security

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KubeSPT: Stateful Pod Teleportation for Service Resilience with Live Migration

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IEEE Transactions on Services Computing 2025年第3期18卷 1500-1514页

作者： Zhang, Hansheng Wu, Song Fan, Hao Huang, Zhuo Xue, Weibin Yu, Chen Ibrahim, Shadi Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong China

Container orchestration systems, such as Kubernetes, streamline containerized application deployment. As more and more applications are being deployed in Kubernetes, there is an increasing need for rescheduling - relocating a running pod to different nodes - due to system upgrades, node failures, and load-balancing optimizations. Live migration, which transfers services from source nodes to target nodes with minimal downtime, is the ideal support for rescheduling. However, implementing live migration for pods that run stateful services is challenging, because Kubernetes manages pods as stateless. First, the current pod's network namespace initialization process causes a mismatch in the network state between the migrated pod and internal containers. Second, migrating the memory state results in extended downtime. Third, Kubernetes operations on pods do not consider preserving the state of the pods. Therefore, we propose KubeSPT to achieve live migration of stateful pods in rescheduling scenarios. Firstly, we synchronize the network state of pods and internal containers by controlling packet flow and implement fast service redirection. Secondly, we introduce a Hot Data and Lazy-Restore method for memory restoration to reduce migration downtime. Finally, we decouple pod migration operations from other Kubernetes operations to ensure compatibility with live migration. Experimental results show that KubeSPT reduces downtime by 86%-93% compared to current rescheduling methods. © 2008-2012 IEEE.

关键词： Restoration

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Boosting efficiency and stability of 2D alternating cation perovskite solar cells via rational surface-modification: Marked passivation efficacy of anion

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Journal of Energy Chemistry 2023年第9期84卷 354-362页

作者： Hualin Zheng Xuefeng Peng Tingxi Chen Ting Zhang Shihao Yuan Lei Wang Feng Qian Jiang Huang Xiaodong Liu Zhi David Chen Yanning Zhang Shibin Li State Key Laboratory of Electronic Thin Films and Integrated Devices and School of Optoelectronic Science and EngineeringUniversity of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China(UESTC)Chengdu 610054SichuanChina Department of Electrical&Computer Engineering and Center for Nanoscale Science&EngineeringUniversity of KentuckyLexingtonKY 40506USA Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan 523808GuangdongChina

Two-dimensional(2D) alternating cation(ACI) perovskite surface defects,especially dominant iodine vacancies(V_Ⅰ) and undercoordinated Pb^(2+),limit the performance of perovskite solar cells(PVSCs).To address the issue,1-butyl-3-methylimidazolium trifluoro-methane-sulfonate(BMIMOTF) and its iodide counterpart(BMIMI) are utilized to modify the perovskite surface *** find that BMIMI can change the perovskite surface,whereas BMIMOTF shows a nondestructive and more effective defect passivation,giving significantly reduced defect density and suppressed charge-carrier nonradiative *** mainly attributes to the marked passivation efficacy of OTF-anion on V_Ⅰ and undercoordinated Pb^(2+),rather than BMIMI^(+) *** from the rational surface-modification of BMMIMOTF,the films exhibit an optimized energy level alignment,enhanced hydrophobicity and suppressed ion ***,the BMIMOTF-modified devices achieve an impressive efficiency of 21.38% with a record open-circuit voltage of 1.195 V,which is among the best efficiencies reported for 2D PVSCs,and display greatly enhanced humidity and thermal stability.

关键词： 2D ACI perovskite solar cells Charge-carrier nonradiative recombination Surface defects passivation Energy level alignment Ionic migration Stability

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Exploring Multi-Layer Perceptrons for Time Series Classification in Networks 5th

Exploring Multi-Layer Perceptrons for Time Series Classifica...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Belwal, Ankit Senthilkumar, S. Alam, Intekhab Jaison, Feon Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Modern-day time series classification (TSC) in networks has emerged as famous and is presently an energetic research location. However, traditional methods in TSC in networks, including okay-nearest neighbors and help vector machines, require large training units and an extensive computational attempt as they need more capacity to extract complicated capabilities. This paper explores Multi-Layer Perceptrons (MLPs) as an alternative records-pushed approach to tackle the TSC task. We first design experiments to benchmark the performance of trendy MLPs educated on representative time series datasets from diverse domain names. We then look at the different architectures of state-of-the-art MLPs for the TSC hassle by exploring contemporary activation functions and structure variations. In addition, we speak about how distinct initialization techniques and hyperparameters may impact the schooling state-of-the-art MLP version. The outcomes reveal the higher accuracy of the MLPs in TSC networks. Eventually, we gift viable traces of the latest similar investigation for the proposed method. The technical summary explores using multi-layer perceptrons (MLP) for network time collection classification. MLP is a supervised deep mastering class set of rules used to expect time series lessons from entered data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series

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Machine Learning-based Framework for Malware Detection in Critical Infrastructures for Smart Cities

Machine Learning-based Framework for Malware Detection in Cr...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Bhatt, Aditya Dasadiya, Sujal Gohil, Aditya Gupta, Rajesh Kumar Jadav, Nilesh Tanwar, Sudeep Garg, Deepak Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India SR University School of Computer Science and AI Telangana Warangal India

ISBN: (纸本)9798350361537

technology is an inseparable part of today's world and is extensively used in every conceivable domain to provide a wide range of applications. technology has many critical infrastructures, which serve the function of delivering crucial services essential for the smooth functioning of smart cities. These infrastructures produce a large amount of data which is extremely sensitive and very potentially catastrophic if it falls into the wrong hands. Various techniques and concepts have been introduced and developed to deal with the detection of malicious data. But with the passing time, malware threats grow in both frequency and complexity, machine learning and artificial intelligence techniques can be combined to enhance malware detection. Many Machine Learning algorithms exist which may be confusing for users. Different algorithms vary in their results, accuracy, and prediction capabilities depending on the data they are trained and tested on. For data produced by critical infrastructures, the prediction must be as accurate as possible. Encouraged by the aforementioned facts, we propose an approach aiming to contrast and compare different machine learning algorithms for malware detection on portable executable files dataset. Different performance metrics are compared for all the considered algorithms, based on which, the best algorithm for critical infrastructure malware detection can be determined. © 2024 IEEE.

关键词： Adversarial machine learning

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