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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ms. Jyoti Shekhawat Ms. Rashmi Khurana Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri India

The study introduces OutGene, a method for detecting malicious behaviour in real time without prior knowledge of assaults or training data. OutGene gathers hosts with comparable behaviour via clustering. We offer the idea of genetic zoom, which involves utilizing a genetic algorithm which select the attributes that is more pertinent to characterize cluster in order to aid human analysts in locating malevolent clusters. Attacks carried out slowly or below thresholds are frequently successful in evading machine learning-based attack detection. We offer the idea of time stretching as a way to identify such stealth attacks. The goal is to discover assaults regardless of how quickly they are carried out by analyzing the stream of events over various time frames. We evaluated OutGene experimentally using data sets currently publicly available and data sets collected at critical military installations. Both genetic zoom and temporal stretching achieved high recall and precision scores, indicating that both are effective.

关键词： Learning systems Training data Genetics Real-time systems Informatics Genetic algorithms

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SketchConf: A Framework for Automatic Sketch Configuration

SketchConf: A Framework for Automatic Sketch Configuration

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International Conference on Data engineering

作者： Ruijie Miao Fenghao Dong Yikai Zhao Yiming Zhao Yuhan Wu Kaicheng Yang Tong Yang Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China

Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches under the expected error constraints. In this paper, we explore how to configure sketch parameters to satisfy user-defined error constraints. We propose SketchConf, an automatic sketch configuration framework, which efficiently generates memory-optimal configurations for the first time. We show that SketchConf can be applied to order-independent sketches, including CM, Count, Tower, and Nitro sketches. We further discuss how to deal with the unknown and changeable workloads when applying SketchConf to the real scenarios of streaming data processing. Experimental results show that SketchConf can be up to 715.51 times faster than the baseline algorithm, and the outputted configurations save up to 99.99% memory and achieve up to 27.44 times throughput, compared with the theory-based configurations. The code is open sourced at Github.

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Analysis of the Effects to Recognize the use of Effective Green Cloud-Based Solutions Using Tiny Smart Grid to Light Web Data Centres

Analysis of the Effects to Recognize the use of Effective Gr...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Rajat Kumar Mizan Ali Khan Department of Master of Computer Application Chandigarh School of Business Jhanjeri Department of Computer Science Engineering Sanskriti University Mathura Uttar Pradesh India

ISBN: (纸本)9781665487351

Recent years have seen two significant advancements in the ICT industry: green technology and data storage. According to going green, the information and communication technology (Cmc) industry now consumes a significant amount of energy and thus produces a significant amount of CO2 emissions. IT gear may be purchased as an utility via cloud services with no up-front costs. Based on previously reported studies that demonstrate that csps are the most ecofriendly option for delivering, the intersection of these two advances cloud be investigated first in this study. Outsourcing private cloud hosting enables companies to run more efficiently. Three instances of innovative businesses offering sustainable web hosting are also offered, and the factors affecting their resource growth are looked at. The results demonstrate that there is no longer a need for green virtual private servers since low costs are the main driver of company decisions. Environmental network operators should create green service levels, negotiate on lushness, and use unambiguous economic measures to show their vegetation. Therefore, it is up to affected customers to push for more environmentally friendly internet services, which will increase the need for green public cloud and sustainable software solutions.

关键词： Industries Cloud computing Data centers Costs Green products Web and internet services Companies

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A Method of Realizing External Network Access to Intranet for Embedded Web Server 2

A Method of Realizing External Network Access to Intranet fo...

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2nd International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2022

作者： Zhang, Xian Qu, Taiguo Tang, Pengju Liu, Yiwen Key Lab. of Intelligent Contr. Technol. for Wuling Mountain Ecological Agriculture in Hunan Province Huaihua China Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling Huaihua China College of Computer Science and Electronic Engineering Hunan University ChangSha China School of Computer Science and Engineering Huaihua University Huaihua China

ISBN: (纸本)9781665464017

Based on the application requirements of the embedded Web server for external network access to the internal network, this paper designs and builds an embedded Web server scheme for external network access to the internal network based on the embedded Boa server and phddns program. This solution mainly transplants the Boa server and phddns program in embedded devices. Boa provides a Web server application function, and phddns software realizes the mapping and binding mapping between the IP address and the domain name of embedded devices in the intranet. An experiment is designed to test this scheme's connection success rate, response time, stable running time, domain name mapping success rate, CPU utilization and other performance indicators. The test results show that this scheme can better meet the system requirements of external network access to the internal network. Therefore, this scheme can support the information and intelligent monitoring and control of embedded devices and has good practical value and application prospects. © 2022 IEEE.

关键词： Intranets

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Real-Time Data Streams: The Future of Predictive Maintenance in Manufacturing

Real-Time Data Streams: The Future of Predictive Maintenance...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Manish Kumar Goyal Haripriya Akhilendra Pratap Singh Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

Dynamic Predictive Modeling is a method that has brought about a revolutionary change in the field of preventive maintenance in the industrial sector. Using the benefits of real-time data streams and flexible algorithms, this cutting-edge technology enhances predictive maintenance processes. In this investigation, we evaluate the efficacy of Dynamic Predictive Modeling to that of numerous more conventional methods, including vibration analysis, acoustic emission analysis, thermography, oil analysis, ultrasonic testing, and data analytics. Accuracy, resource optimization, adaptability, downtime reduction, cost-effectiveness, and preventative maintenance are just a few of the criteria we use to evaluate the methods. The findings indicate that Dynamic Predictive Modeling outperforms its predecessors across a range of performance metrics. It can better adapt to the ever-changing industrial environment, allowing for more precise and timely breakdown forecasts. The allocation of resources and the economy of operation are both enhanced by the fact that maintenance procedures may be adjusted according to the actual condition of the equipment. Predictive maintenance is the most effective method for minimizing unscheduled downtime and increasing output in heavy industry. In a comparison of several approaches to predictive maintenance, Dynamic Predictive Modeling comes out on top with the highest aggregate score. A study reveals how an unconventional approach might improve the effectiveness and reliability of industrial operations. Increased equipment reliability, decreased maintenance costs, and a potential advantage in the manufacturing industry might arise from the use of dynamic predictive modeling.

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CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition

arXiv

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arXiv 2024年

作者： Wang, Yifan Gui, Jie Tang, Yuan Yan Kwok, James Tin-Yau School of Cyber Science and Engineering Southeast University Nanjing210000 China Engineering Research Center of Blockchain Application Supervision and Management Southeast University Ministry of Education Purple Mountain Laboratories Nanjing210000 China The Department of Computer and Information Science University of Macao Macao999078 China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong999077 Hong Kong

Finger vein recognition technology has become one of the primary solutions for high-security identification systems. However, it still has information leakage problems, which seriously jeopardizes user’s privacy and anonymity and cause great security risks. In addition, there is no work to consider a fully integrated secure finger vein recognition system. So, different from the previous systems, we integrate preprocessing and template protection into an integrated deep learning model. We propose an end-to-end cancelable finger vein network (CFVNet), which can be used to design an secure finger vein recognition system. It includes a plug-and-play BWR-ROIAlign unit, which consists of three sub-modules: Localization, Compression and Transformation. The localization module achieves automated localization of stable and unique finger vein ROI. The compression module losslessly removes spatial and channel redundancies. The transformation module uses the proposed BWR method to introduce unlinkability, irreversibility and revocability to the system. BWR-ROIAlign can directly plug into the model to introduce the above features for DCNN-based finger vein recognition systems. We perform extensive experiments on four public datasets to study the performance and cancelable biometric attributes of the CFVNet-based recognition system. The average accuracy, EERs and D↔sys on the four datasets are 99.82%, 0.01% and 0.025, respectively, and achieves competitive performance compared with the state-of-the-arts. © 2024, CC BY-NC-SA.

关键词： Boiling water reactors

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Revisiting Aggregation Sequentially with M-A-C’s and Detecting Functionality

Revisiting Aggregation Sequentially with M-A-C’s and Detect...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ms. Ritu Shree Ms. Manjeet Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business Jhanjeri India

A cryptographic tool known as the aggregate message authentication code (aggregate MAC) can condense the MAC’Stags from several messages into a single, condensed aggregate MAC’Stag. The sequentially aggregated MAC’s may additionally verify the (sequential) order of messages in addition to the correctness of multiple messages. In this study, we provide a novel sequentially aggregated MAC’s paradigm in which an aggregation kind of algorithm creates a sequential aggregating based just on any type of various sequences officially describe type of security in this paradigm, which uses separate MAC’Stags without a secret key. Additionally, without altering the MACs’ structure, we provide a general method of building consecutive aggregate MACs beginning from any MAC. By integrating the aggregation algorithm with other MAC’s methods currently present in the given type of networks, this property is helpful to increase the efficiency of the current networks. It also offer a sequential aggregate MAC’s with detecting capabilities by expanding the SAMAC’s findings (SAMD’S). It can define a message which is valid or invalid order for a particular type of message using the SAMD’S. We propose a general type of design of SAMD’S using random type of oracle model from which any MACs and non-adaptive group can be tested and their kind of protocols with formal type of security implementation proofs. We further define the type of security of SAMD’S.

关键词： Protocols Codes Aggregates Buildings Security Cryptography Informatics

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Improvised Text Classification by Hybrid Machine Learning Techniques

Improvised Text Classification by Hybrid Machine Learning Te...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Cuddapah Anitha Akanksha Kapruwan Shilpa M Jyoti Dange Vipul Vekariya Ramya Maranan Department of Computer Science and Engineering School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Application RNS First Grade College Channasandra Bangalore Karnataka India Department of Information Technology Atharva College of Engineering Malad West Mumbai Maharashtra India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

To improve the performance of English-text classification, text classification systems aim to design appropriate algorithms for extracting features and auto-classifying texts with automatic semantic feature selection. Motivated by the efficiency of Attention mechanism (AM), the ways to use AMs at different levels in CNN and specific RNN organization are explored in this work and BLSTM based models to eliminate misinformation, obtain more accurate contextual information, and ultimately improve performance. Finally, all of the features are concatenated and fed into the softmax classifier. The BLSTM is primarily used to hold the sentence interdependencies, then CNN is selected to extract features for continuous modeling operations, and this model is evaluated by applying it to the ETC. Furthermore, the proposed integrated CNN and BLSTM with Attention mechanism (A-C-BLSTM) model achieves significant results through our evaluation and performs better than a wide range of baseline models.

关键词： Technological innovation Attention mechanisms Computational modeling Text categorization Semantics Organizations Feature extraction

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Deep Learning-Based Diagnostic Tool for Brain Tumours

Deep Learning-Based Diagnostic Tool for Brain Tumours

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Arun Singh Manik Rakhra Dalwinder Singh Arvind Sharma Rakesh Kumar Department of Computer Science & Engineering Lovely Professional University Jalandhar Phagwara India School of Computer Application Lovely Professional University Phagwara Punjab India Department of Mathematics Lovely Professional University Phagwara Punjab India

In this work, we provide the results of our experiments on Brain Tumour Classification and explore their implications. MRI, short for magnetic resonance imaging, is a crucial piece of medical imaging technology that creates high-resolution pictures of the inside of a human body by combining radio waves, a strong magnetic field, and a computer. In this study, we employ a CNN-based Deep learning model to categorise cases of brain tumours. Common applications for CNNs (Convolutional Neural Networks) include video and image analysis. It processes images by first ingesting them and then convoluting them into smaller and smaller pieces. The convolutional layer analyses the input picture using a series of filters designed to identify elements like edges, curves, and patterns. Here, we constructed the CNN using data obtained from Kaggle. It contains roughly 3300 MRI scans of the brain, split into 4 groups (no tumours, meningioma tumours, glioma tumours, and pituitary tumours) and a total of 384 testing pictures. We used Keras to construct the model. The accuracy of the suggested project is quite high, coming in at 97.142%. The Confusion matrix is used to evaluate the precision. It is a data table for measuring the efficacy of an ML system. This provides the results of a comparison between the actual values in a dataset and the model's projected values in an easy-to-understand format.

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Machine Learning and Data Mining for Predictive Modeling of Stroke Risk and Post-Stroke Care Management

Machine Learning and Data Mining for Predictive Modeling of ...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Gobi N B.P. Singh Surendra Yadav Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

The research investigates the efficacy of a comprehensive machine learning and data mining approach in predictive modeling of stroke risk and post-stroke care management. A holistic methodology is proposed, integrating diverse datasets and advanced algorithms, including Logistic Regression, Random Forest, and Long Short-Term Memory (LSTM) networks. Data preprocessing, feature extraction, and personalized care plan generation are key components. Performance evaluation using accuracy, sensitivity, specificity, precision, F1 Score, AUC, and MAE demonstrates the superior predictive capabilities and efficiency of the proposed method compared to traditional approaches. The study highlights the potential for revolutionizing stroke care, emphasizing personalized risk assessment and post-stroke management for improved patient outcomes and healthcare delivery.

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