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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. Sakthivelan, R.G. Subha, S. Sree Gopirajan, P.V. Thandalam Chennai India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India

ISBN: (纸本)9798331300579

Every year, millions of students graduate, making it difficult to track and process their data. The One Point Student Verification Machine learning Model (OSVMLM) collects rich data on students, including biometric information, secondary education details, list of degrees completed, and detailed information on their last or current degree. In addition, the student's fingerprint is also collected and stored. The government's Aadhar database is used to verify the authenticity of the student. To ease the recruiter, the details collected by OSVMLM were assigned with ranks, information will be sent to the candidates autonomously based on the current job description. This makes it easy for students to identify and apply for available positions. OSVMLM application is built on Ionic, MongoDB, NodeJS and Gradient boosting machines (GBMs). OSVMLM is immensely useful for recruiters, as it makes data collection and processing effortless. This increases the visibility of various jobs open in different domains and suggests suitable job description to the job seekers. The key difference between OSVMLM and other platforms is that it is not limited to institutions and is accessible to all. This saves a lot of time for recruiters during initial screening. This proposed OSVMLM was validated with the real-time students dataset and achieved 91% prediction accuracy. © Grenze Scientific Society, 2024.

关键词： Students

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Monogamy and polygamy for the generalized W-class states using unified-(q,s)entropy

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science China(Physics,Mechanics & Astronomy) 2024年第1期67卷 10-22页

作者： Bo Li Bing Xie Zhanjun Zhang Heng Fan School of Computer and Computing Science Hangzhou City UniversityHangzhou 310015China Department of Mathematics East China University of TechnologyNanchang 330013China School of Information&Electronic Engineering(Sussex Artificial Intelligence Institute) Zhejiang Gongshang UniversityHangzhou 310018China Institute of Physics Chinese Academy of SciencesBeijing 100190China

We describe the entanglement distribution and restricted shareability of the multipartite generalized W-class states and their reduced density matrix under arbitrary partitions by using monogamy and polygamy relation based on the unified-(q,s)***,we provide an analytical formula of unified-(q,s)entanglement(UE)and an analytical lower bound of unified-(q,s)entanglement of assistance(UEo A)for a reduced density matrix of a generalized W-class ***,we use these two analytical formulas to derive the monogamy and polygamy inequalities for a reduced density matrix of a qudit generalized W-class(GW)*** establish two partition-dependent residual entanglements based on the new monogamy relation,which is helpful to obtain a comprehensive analysis of entanglement dynamics of generalized W-class ***,we investigate tighter monogamy and polygamy relations based on the power ofαth(α≥0)for UE andβth(β≥0)for UEo A,*** results show that the entanglement distribution characteristics of generalized W-class states satisfying stronger constraints can be described more accurately.

关键词： monogamy polygamy multiqudit unified entropy

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ML assisted enhanced recommendation system for reliable volunteering environment 2

ML assisted enhanced recommendation system for reliable volu...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Prabakaran, N. Meenakshi, K. Kumarnath, J. Rajasekaran, P. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Mathematics Presidency University Bengaluru560064 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul624005 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu603203 India

The coronavirus pandemic has negatively impacted everyone around the globe. The need to follow social distancing has increased the demand for technology that makes remote volunteering accessible. Alerting, aiding, and educating individuals at risk in a privacy-preserving manner are the need of the hour. Several NGOs have taken up this responsibility but are lacking the resources to reach willing volunteers and donations. This project attempts to make a novel on-demand service application using ID3, CART, and C4.5 decision tree classification algorithms and compare their accuracies. Algorithm C4.5 here we use as a Decision Tree classifier that can be make decisions based on univariate or multivariate predictor. C4.5 Algorithm builds decision trees based on the conception of information gain, with the decision of each classification associated with the target classification. The best way to determine uncertainty is to use entropy that reflects the basis of decision. We aim to classify volunteer data based on predicted volunteer reliability. C4.5 algorithm shows 11.63 higher accuracy when compared to other algorithm and the value of 0.36 Gini index for the C4.5 indicates that there is an adequate equality. It is conferred that the C4.5 algorithm will best solve this problem based on the required dataset that we have created. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision trees

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A Sentimental Analysis Approach for Personalized Drug Recommendations Using Machine Learning 1

A Sentimental Analysis Approach for Personalized Drug Recomm...

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1st IEEE International Conference on computing and Data science, ICCDS 2024

作者： Vijayaraj, A. Murugan, V.P. Jebakumar, R. Gaurav, N.V. Santhosh, V. Shai Kumar, R. R.M.K. Engineering College Department of Information Technology Chennai India Panimalar Engineering College Department of Mathematics Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350365337

Since the virus that causes corona disease was discovered, it has gotten more difficult to find medical professionals with the appropriate licenses, including doctors, nurses, diagnostic equipment, and medications. Due to the intense grieving, a substantial number of people in the medical industry pass away. Because of the scarcity, a lot of individuals began self-medicating without first consulting a doctor, which made the health problem worse. There are numerous well-established applications for machine learning, and recent years have witnessed a growth in the pace and breadth of computerization-related research and development. The goal of this research is to develop a method for recommending medications that might greatly reduce the obligations of specialists. In this work, we built a drug recommendation system that makes estimations about the emotional of patient reviews using a variety of different vectorization techniques, such as using a Bow, The TF-ID Word 2 Vector, or Manually Feature Analysis. The predicted emotions were graded using accuracy, remembrance, flscore, exactness, and the area beneath the slope (AUC). The results show that, when weighed against the other models, the classification technique Linear SVC + The TF -I vector process has the greatest accuracy. © 2024 IEEE.

关键词： Recommender systems

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Real-World Wireless Network Modeling and Optimization:From Model/Data-Driven Perspective

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Chinese Journal of Electronics 2022年第6期31卷 991-1012页

作者： LI Yang ZHANG Shutao REN Xiaohui ZHU Jianhang HUANG Jiajie HE Pengcheng SHEN Kaiming YAO Zhiqiang GONG Jie CHANG Tsunghui SHI Qingjiang LUO Zhiquan Shenzhen Research Institute of Big Data Pengcheng Lab School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) College of Mathematics and Computational Sciences Xiangtan University School of Computer Science and Engineering Sun Yat-Sen University School of Software Engineering Tongji University

With the rapid development of the fifth-generation wireless communication systems, a profound revolution in terms of transmission capacity, energy efficiency, reliability, latency, and connectivity is highly expected to support a new batch of industries and applications. To achieve this goal, wireless networks are becoming extremely dynamic, heterogeneous, and complex. The modeling and optimization for the performance of realworld wireless networks are extremely challenging due to the difficulty to predict the network performance as a function of network parameters, and the prohibitively huge number of parameters to optimize. The conventional network modeling and optimization approaches rely on drive test, trial-and-error, and engineering experience,which are labor intensive, error-prone, and far from optimal. On the other hand, while the research community has spent significant efforts in understanding the fundamental limits of radio channels and developing physical layer techniques to operate close to it, very little is known about the performance limits of wireless networks, where millions of radio channels interact with one another in complex manners. This paper reviews the very recent mathematical and learning based techniques for modeling and optimizing the performance of real-world wireless networks in five aspects, including channel modeling, user demand and traffic modeling, throughput modeling and prediction, network parameter optimization, and IRS empowered performance optimization, and also presents the corresponding notable performance gains.

关键词： network parameter optimization telecommunication computing optimisation mathematical techniques wireless channels performance optimization learning based techniques telecommunication traffic mathematical analysis physical layer techniques channel modeling wireless network modeling fifth generation wireless communication systems learning (artificial intelligence) model-data-driven perspective IRS empowered performance optimization radio channels traffic modeling radio networks

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FADA: Flooding Attack Defense AODV Protocol to counter Flooding Attack in MANET 5

FADA: Flooding Attack Defense AODV Protocol to counter Flood...

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5th IEEE International Conference on Advances in Electronics, Computers and Communications, ICAECC 2023

作者： Kiruthika, M. Krishnasamy, Lalitha Samuel, Prithi Kanagaraj, Kaavya School of Computing SRM Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankulathur India School of Engineering and Technology Christ University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350305449

The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration. © 2023 IEEE.

关键词： Mobile ad hoc networks

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Detection and Classification of AI-Generated Text 15

Detection and Classification of AI-Generated Text

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Gupta, Shreya Gupta, Deepa Indian Institute of Technology Delhi Department of Mathematics Delhi India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The advent of Large Language Models (LLMs), has largely transformed global interactions with artificial intelligence. These models, though useful in tasks ranging from translation to text generation, have now permeated everyday life, thereby raising concerns about their potential misuse, particularly in educational institutions. Despite measures like access restrictions implemented by educational institutions, AI based plagiarism has continued to grow, with existing plagiarism detection tools exhibiting limited efficacy in curbing the problem. This paper addresses the need for better AI detection algorithms, focusing on the detection of subtler forms of plagiarism facilitated by LLMs. Leveraging insights from existing studies, the research explores the efficacy of various boosting based machine learning models and dimensionality reduction techniques in improving the accuracy of detecting AI-augmented essays. © 2024 IEEE.

关键词： AI in Education ChatGPT Natural Language Processing plagiarism detection

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Triple-Path RNN Network: A Time-and-Frequency Joint Domain Speech Separation Model 24th

Triple-Path RNN Network: A Time-and-Frequency Joint Domain S...

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24th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2023

作者： Zhai, Yu-Huan Hua, Qiang Wang, Xiao-Wen Dong, Chun-Ru Zhang, Feng Xu, Da- Chuan College of Mathematics and Information Science Hebei University Baoding071002 China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819982103

Studies in speech separation have achieved significant success in recent years. To correctly separate the mixture signals, it is critical to encode the signals into an appropriate latent space. Existing speech separation methods include transforming mixed signals into frequency domain space or time domain space. The frequency domain features (spectrogram) are generated by STFT, which is closely related to speech articulation and reflects the energy of speech directly. The time domain features are learned from a latent embedding space, and the separation effect is facilitated by the end-to-end structure. However, these methods are based on the representations from only one domain, which is insufficient for providing a speech separation encoding space that is completely separable. Therefore, a Triple-Path Recurrent Neural Network (TPRNN) that fuse features from two domains is proposed. It employs a spectrogram as auxiliary information to improve the performance of speech separation. Experimental results on the Wall Street Journal (WSJ0) dataset show that this approach is beneficial to improve speech separation performance. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Frequency domain analysis

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Design of Query Based Gallery Selector and Mask-Aware Loss for Person Search 24th

Design of Query Based Gallery Selector and Mask-Aware Loss f...

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24th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2023

作者： Hua, Qiang Sun, Ao Liu, Yu-Chen Zhang, Feng Dong, Chun-Ru Xu, Da-Chuan College of Mathematics and Information Science Hebei University Baoding071002 China Beijing Institute for Scientific and Engineering Computing Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819982103

Person search is a challenging computer vision task that aims to simultaneously locate and identify a query person from panoramic images. To address the issue of scene similarity and its impact on search accuracy and efficiency, we propose a query based gallery selector module that employs cosine similarity to calculate the similarity between candidate images in the gallery and the query persons feature embedding, then selects and reorders images in the gallery based on their similarity to the query person, thus improving the accuracy and efficiency of searching. Furthermore, we introduce a mask-aware mechanism that improves the localization loss function for predicted bounding boxes. During training, the network is guided to increase its robustness in occluded scenarios. Experimental results on public person search datasets PRW and CUHK-SYSU demonstrate the effectiveness of our proposed method. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Development of A Multipurpose Healthcare Mobile App Using JAVA

Development of A Multipurpose Healthcare Mobile App Using JA...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Pongiannan, R.K. Bushra, S. Nikkath Lathif, Syed Ismail Abdul Pradhan, Saahil Brindha, R. Franklin, J. Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur India Data Science and Business Systems School of Computing Srm Institute of Science and Technology Kattankulathur India Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India Srm Institute of Science and Technology Department of Electrical and Electronics Engineering Kattankulathur India

ISBN: (纸本)9798350315127

Health care has become an essential part of present human life. The preliminary requirement to select the basic information about medical facilities through a mobile app will serve the needs of the common people. This paper presents an integrated mobile app developed using JAVA. This app is user-friendly in terms of login, registration, and bookings. This caters to the facility to select information regarding doctors, medical lab facilities, pharmacy details, and health articles. This app will provide communication. © 2023 IEEE.

关键词： Medicine

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