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A n empirical study to examine the student activity analysis components of technology using an extended multi-labeled gradient boosting methodology

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Materials Today: Proceedings 2023年 81卷 620-624页

作者： R. Renuga Devi B. Suresh Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai.Tamilnadu India

The data surveying this study mines methods for extracting informative and interesting knowledge from learning student datasets. The amount of data stored in education big databases is growing at an alarming rate. As such, data processing and knowledge discovery techniques can be used to discover interesting relationships between different characteristics and assessments of students. Initially to propose a novel self-analysis model which performs analysis about the different big data learning services. From the result provided by the user, the service will be ranked and analyze about the knowledge from daily activity gathered by the learner. The proposed model performs analysis to provide efficient Educational Data Mining (EDM) E-Learning system based on machine learning to ranking the users and performance evaluation in their activities. The existing method does not consider the learner activities, ranking and it focus on single feature based classification so the system doesn’t provide a better classification result. These issues to overcome introduce an Extended Multi-Labeled Gradient Boosting (XMGB) improve the classification performance both of imbalanced dataset. The method initial stage apply the Adaptive Expectation-Maximization Mixture (AEMM) method cluster the data various group and extract the E-learning multiple feature set. In this method to compute the classification time and memory provide an efficient result. Then introduce an academic activity based weight score analysis model to evaluate the learner daily activates and exam score its help to the ranking and cluster the data groups. In this proposed method analysis result to evaluate the following performance matrix: Recall, precision, F measure, classification accuracy. In this simulation analysis of the proposed method XMGB are provide an efficient classification with less time complexity compared to other existing methods.

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Enhancing Non-Fungible Tokens for the Evolution of Blockchain Technology

Enhancing Non-Fungible Tokens for the Evolution of Blockchai...

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2022 International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2022

作者： Khan, Farhan Kothari, Rakshit Patel, Mayank Banoth, Niharika Geetanjali Institute Technical Studies Department of Computer Science Udaipur India

ISBN: (纸本)9781665478847

Non-fungible tokens (NFTs) are a new sort of blockchain-based token that is unique and indivisible. They were first announced in late 2017. NFTs are a type of blockchain-based virtual asset that has sparked a lot of investor interest in a very short amount of time. Since early 2021, the phenomena and its marketplaces have risen dramatically. The blockchain revolution is rolling forth a slew of changes that the art world may take advantage of. The purpose of this paper is to provide extensive information on the NFT, including its application, method of operation, buying, creating, and selling procedures, as well as its use. The NFT when paired with Metaverse, represents a significant advancement and revolution in the realm of virtual reality and blockchain, giving artists a new avenue to express their unique and valuable work. This study fills in the conceptual and practical knowledge gaps by demonstrating the usefulness of NFTs in the realms of substantial development and revolution in new private and public blockchain application. © 2022 IEEE.

关键词： Blockchain

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A Hybrid Methodology for Software Development and IT Team Analysis in Manufacturing 1

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

The IT team working in the manufacturing industry may use this paper to learn about the results of the recently established hybrid software development model with the aid of this paper. The IT team’s needs, difficulties, working practices, past working strategies, and the client’s expectations in the manufacturing firm all play a role in determining how the hybrid model is implemented. The IT team was able to accomplish goals such as on-time delivery with quality improvement, more accuracy, reduced maintenance cycle, the involvement of clients, reduced development time, and improved defect removal efficiency by utilizing a new model and adopting appropriate features from Traditional and Agile methodologies. This article presents a guideline on prior outcomes for the IT team at a manufacturing business and results after using a hybrid approach. The results of deploying a hybrid software development paradigm inside an in-house IT team that works in the manufacturing business are the subject of this research, which explores those results. This study intends to evaluate the effects of combining conventional and agile approaches for software development by thoroughly examining software development practices and determining how successful this combination is. The research investigates the subsequent enhancements in project delivery deadlines, product quality, and team cooperation. The findings provide light on the possible advantages and drawbacks of using a hybrid method. The results give organizations who are looking to optimize their software development processes in a manufacturing setting with useful insights that will eventually contribute to increased efficiency and competitiveness in the sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Efficiency

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Test Case Optimization Through Ant Colony Optimization Enabled Boosted Regression Model

Test Case Optimization Through Ant Colony Optimization Enabl...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Manojkumar V R. Mahalakshmi Department of Computer Applications Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The avalanche of data creates complex operational issues in real world scenarios. In terms of making the test procedures simple and effective test case optimization techniques are applied. To achieve better test case performance, the proposed system focused on creating a model centric method that comparatively overheads the test case optimization works. The presented system considers the comparative evaluation of test case optimization of MIS dataset. The methods such as Linear regression (LR), Customized Random Forest regression (RFR), the robust Gradient boost regression (GBR), dynamically working Support vector machine (SVM) and proposed Ant colony optimized Boosted regression model (ACO-BRM). The proposed approach exposed to the benefit of nature inspired optimization model named ant colony and optimized the test cases with boosted regression model and achieved the minimum of mean absolute error of 0.34 comparatively with existing state of art approaches. The priority score is enhanced after the optimization process.

关键词： Support vector machines Deep learning Machine learning algorithms Computational modeling Optimization models Linear regression Data models Surges Optimization Random forests

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Exploring Human Gesture Recognition: Bridging Communication Gaps Through Dynamic Signal Analysis 3rd

Exploring Human Gesture Recognition: Bridging Communication ...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Sheikh, Anisha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (纸本)9789819751457

The ability of humans to communicate more effectively via the use of gestures is widely recognised as one of the most important aspects of human interaction. Because of the inextricable link that exists between body language and successful communication, there has been a significant amount of study conducted in the domains of computer vision and human–computer interaction (HCI). Signal recognition is one of the most important aspects of this study. Signal recognition attempts to analyse and decode the information that is transmitted by human motions. These movements include gestures such as hand and facial expressions. Signal recognition is a key bridge that enables people with varying skills to successfully communicate and connect with others. This is particularly true for those who have trouble understanding speech. There is a possibility that algorithms developed for signal detection can help bridge the communication gap and make interactions more welcoming to all parties involved. These attempts constitute a big step forward in the direction of making interactions with computers seem more natural and more analogous to communication between humans. Static signals and dynamic signals are the two basic types that may be distinguished within the field of signal recognition. These critical frames act as turning points, summing together the most important aspects of the meaning sent by the signal. The purpose of this study is to investigate the area of dynamic signal identification and key frame extraction in order to shed light on the advantages and disadvantages associated with both of these processes. By investigating these facets, our ultimate goal is to get a more profound comprehension of the complexities involved in interpreting dynamic gestures and the possible uses of these gestures. This understanding will add to the continuing attempts to make interactions between humans and computers more natural, inclusive, and human-like, which will eventually improve th

关键词： Gesture recognition

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Hyperspectral Images for Crop Prediction using Machine and Deep Learning Models

Hyperspectral Images for Crop Prediction using Machine and D...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： R. Mahalakshmi K. Kumutha M. Sakthivanitha R. Bagavathi Lakshmi Mohamed Sirajudeen Sankar Padmanabhan Department of Computer Applications-PG Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Computer Applications-PG Vels Institute of Science Technology Advanced Studies Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India Department of Information Technology Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India PG and Research Department of Computer Science Nilgiri College of Arts and Science The Nilgiris Tami Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Crop classification is essential for local and national governments to make informed agricultural decisions. Remote sensing technology has made it possible to employ high-resolution hyperspectral images (HSIs) for land cover classification for decades. Machine Learning (ML) and Deep Learning (DL) are becoming more popular for HSI categorization due to advances in processing capacity and technology. HSIs can accurately identify crop types due to their narrow and continuous spectral band reflection. This study compares traditional machine learning (ML) models like Support Vector Machines (SVM) and Random Forest (RF) to deep learning (DL) models like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for hyperspectral image classification (HSI). The HSI categorization was evaluated using performance criteria such as accuracy, precision, recall, and the F1-Score. CNN achieved the highest accuracy of 96%, demonstrating that it is the most reliable solution for complex HSI classification tasks because it successfully captures spatial and spectral data.

关键词： Support vector machines Radio frequency Deep learning Recurrent neural networks Accuracy Crops Spatial databases Convolutional neural networks Reliability Hyperspectral imaging

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Anti-Forensic Technique Using Alternate Data Streams for Data Hiding in NTFS 1

Anti-Forensic Technique Using Alternate Data Streams for Dat...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Hermon, Rahul Singh, Upasna Singh, Bhupendra Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India Indian Institute of Information Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

Data hiding techniques are one of the most commonly used anti-forensic methods by attackers or perpetrators. There are many data hiding techniques in various kinds of file system in different OS. Alternate data streams (ADS) in NTFS are one such means by which data can be hidden in Windows OS. In this work, ADS will be used to demonstrate how data can be hidden in it, to include different kinds of data files. The same will be implemented using command prompt. Lastly, we compared the data hiding technique using ADS in Windows 10 and 11 Operating System. © 2022 IEEE.

关键词： Windows operating system

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Dual-logic-in-memory implementation with orthogonal polarization of van der Waals ferroelectric heterostructure

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InfoMat 2024年第2期6卷 107-117页

作者： Jingjie Niu Sumin Jeon Donggyu Kim Sungpyo Baek Hyun Ho Yoo Jie Li Ji-Sang Park Yoonmyung Lee Sungjoo Lee SKKU Advanced Institute of Nanotechnology(SAINT) Sungkyunkwan UniversitySuwonRepublic of Korea Department of Nano Science and Technology Sungkyunkwan UniversitySuwonRepublic of Korea Department of Electrical and Computer Engineering Sungkyunkwan UniversitySuwonRepublic of Korea IMEC HeverleeBelgium Department of Nano Engineering Sungkyunkwan UniversitySuwonRepublic of Korea

The rapid advancement of AI-enabled applications has resulted in an increasing need for energy-efficient computing ***-in-memory is a promising approach for processing the data stored in memory,wherein fast and efficient computations are possible owing to the parallel execution of reconfigurable logic *** this study,a dual-logic-in-memory device,which can simultaneously perform two logic operations in four states,is demonstrated using van der Waals ferroelectric field-effect transistors(vdW FeFETs).The proposed dual-logic-in-memory device,which also acts as a twobit storage device,is a single bidirectional polarization-integrated ferroelectric field-effect transistor(BPI-FeFET).It is fabricated by integrating an in-plane vdW ferroelectric semiconductor SnS and an out-of-plane vdW ferroelectric gate dielectric material—CuInP_(2)S_(6).Four reliable resistance states with excellent endurance and retention characteristics were *** two-bit storage mechanism in a BPI-FeFET was analyzed from two perspectives:carrier density and carrier injection controls,which originated from the out-of-plane polarization of the gate dielectric and in-plane polarization of the semiconductor,*** conventional multilevel FeFETs,the proposed BPIFeFET does not require additional pre-examination or erasing steps to switch from/to an intermediate polarization,enabling direct switching between the four memory *** utilize the fabricated BPI-FeFET as a dual-logic-inmemory device,two logical operations were selected(XOR and AND),and their parallel execution was *** types of logic operations could be implemented by selecting different initial states,demonstrating various types of functions required for numerous neural network *** flexibility and efficiency of the proposed dual-logic-in-memory device appear promising in the realization of next-generation low-power computing systems.

关键词： ferroelectric field-effect transistor in-plane ferroelectricity logic-in-memory out-of-plane ferroelectricity

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nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering 7

nmODE-MVC: Neural Memory ODE-Based Multi-View Clustering

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7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

作者： Zheng, Yan Wu, Song Ren, Yazhou Pu, Xiaorong Xu, Zenglin He, Lifang Yi, Zhang University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China University of Electronic Science And Technology of China School of Computer Science And Engineering Shenzhen Institute For Advanced Study China Harbin Institute of Technology Shenzhen School of Computer Science And Technology Shenzhen China Lehigh University Department of Computer Science And Engineering Bethlehem United States Sichuan University Intelligent Interdisciplinary Research Center College of Computer Science Chengdu China

ISBN: (纸本)9798350359145

Deep multi-view clustering (MVC) has recently gained significant interest for its capability to harness complementary information across multiple views through deep neural networks, enhancing clustering performance. However, existing deep MVC methods still have two issues: (1) They separate the feature learning objective and the clustering objective, potentially leading to suboptimal embedding features due to the neglect of the global clustering structure. (2) Most works utilize the target distribution obtained by the clustering objective to supervise feature learning. However, they simply sharpen the soft clustering distribution to obtain the target distribution, lacking the dynamics of the clustering process. To address these issues, in this paper we propose a novel end-to-end self-supervised multi-view clustering approach based on NeuralODEs, namely nmODE-MVC. Our approach operates in two stages: In the first stage, all view-embedded features are extracted, and then unified them into a global feature, facilitating global clustering assignments by exploiting multi-view data's complementarity. The second stage leverages the dynamic attributes of NeuralODEs to dynamically refine the data features and generate a target distribution that actively drives the clustering decisions. We formulate these learning processes as a unified optimization problem, facilitating iterative training and refinement. The effectiveness of nmODE-MVC is validated through extensive experiments on multiple real-world multi-view datasets. © 2023 IEEE.

关键词： Ordinary differential equations

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Predicting Air Quality in India Using Machine Learning Models 4

Predicting Air Quality in India Using Machine Learning Model...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Gupta, Swati Kumar, Himanshu Vijarania, Meenu Gautam, Anjali Negi, Neerja Dadhich, Priyanka Kr Mangalam University Department of Computer Science Centre of Excellence Haryana Gurugram India Kr Mangalam University Department of Cse Haryana Gurugram India School of Computer Applications Manav Rachna International Institute of Research & Studies India

ISBN: (纸本)9798350387490

India's poor air quality is responsible for many health concerns, with chronic exposure to fine particulate matter (PM) leading to various health issues. Recently, the pollution levels in India have undergone various changes in the status of pollutants present in the air. Our paper aims to provide a comprehensive analysis of these issues highlighting the severe implications of poor air quality on public health in India and try to predict future health concerns due to poor air quality. In our work we have taken three machine learning modelslinear regression, decision tree regression, and K-Nearest Neighbour into the consideration to train our model. We have evaluated the models by using 'R-squared score' and 'mean square error', to determine which was the best suitable model in predicting the Air quality with better accuracy. Our work contribute to ongoing efforts to improve the air quality forecasts model (AQI) and create effective solutions for a healthier India. © 2024 IEEE.

关键词： Adversarial machine learning

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