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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Hasan, Nadine Kumar, Abhinav Kataoka, Kotaro Indian Institute of Technology Hyderabad Department of Electrical Engineering Hyderabad India Indian Institute of Technology Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331531195

The existing 5G new radio (NR) numerology supports certain values of parameters like subcarrier spacing, symbol duration, and guard interval for vehicle-to-everything (V2X) communications. However, with ever evolving use cases for enhanced-V2X in beyond 5G networks, these recommended values are not sufficient to provide the required quality of service (QoS). Motivated by this, we propose an adaptive numerology scheme (ANS) that dynamically selects these parameters to mitigate the effect of Doppler and multi-path. We present exhaustive simulation results demonstrating the efficiency of the proposed scheme for the dynamic nature of V2X communication networks. © 2025 IEEE.

关键词： Vehicle to Everything

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DRCS: Document Recommendation Framework for Cultural Studies as a Prospective Domain 15th

DRCS: Document Recommendation Framework for Cultural Studies...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Deepak, Gerard Hariharan, Nitin Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Alagappa Chettiar Government College of Engineering and Technology Karaikudi India

ISBN: (纸本)9783031889912

The need for extensions of documents for highly special sensitive domains like cultural studies which involves countries, and which involves cultural relationships and political scenarios between cultures and across countries. As the worldwide web continues to evolve, it is transitioning into a densely populated semantic web, which is considered the standard for the next generation of the web, commonly known as web 3.0. This paper suggests a framework for Document Recommendation framework for Cultural Studies (DRCS) that utilizes methods like term frequency - inverse document frequency (TF-IDF) and LDA (Latent Dirichlet Allocation) to analyze the term data set. Metadata generation is used to collect and arrange the information, employing transformers for classification. The XGBoost classifier, which is feature-controlled, is used for data set classification. To measure semantic relevance, strong similarity measures like APMI (Adaptive Pointwise Mutual Information) and Pianka Index, Ren-Konen Index, Sim Rank are used. The model incorporates LDA for topic modelling and utilizes resources like the Google Knowledge Graph API to access community-verified knowledge. To optimize the solutions, the UnderCricket Algorithm optimization algorithm is employed. The proposed model achieves high percentages of precision, recall, accuracy, and F-measure, specifically 94.75%, 96.74%, 95.745%, and 95.7346598% respectively. Additionally, the model achieves a low value of FDR (False Discovery Rate) at 0.06% is achieved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Recommender systems

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Novel Face Mask Detection Technique using Machine Learning to control COVID’19 pandemic

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Materials Today: Proceedings 2023年 80卷 3714-3718页

作者： Gupta, Sandeep Sreenivasu, S.V.N. Chouhan, Kuldeep Shrivastava, Anurag Sahu, Bharti Manohar Potdar, Ravindra Department of Electrical Engineering JECRC University Jaipur India Department of Computer Science and Engineering Narasaraopeta Engineering College Andhra Pradesh Narasaraopet India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Lakshmi Narain College of Technology and Science Indore India Department of Computer Science and Engineering Dr. D.Y. Patil Institute of Technology Pimpri Pune India Department of Electronics and Telecommunication Engineering Bhilai Institute of Technology Chhattisgarh Durg India

The COVID-19 pandemic has been scattering speedily around the world since 2019. Due to this pandemic, human life is becoming increasingly involutes and complex. Many people have died because of this virus. The lack of antiviral drugs is one of the reasons for the spreading of COVID-19 virus. This disease is spreading continuously and easily due to some common mistakes by people, like breathing, coughing and sneezing by infected persons. The main symptom is the normal flu. Therefore, in the present condition, the best precaution for this disease is the face mask, which covers both areas of mouth & nose. According to the government and the World Health Organization, everyone should wear a face mask in busy places like hospitals and marketplaces. In today's environment, it's difficult to tell if someone is wearing a mask or not, and physical inspection is impractical since it adds to labour costs. In this research, we present a mask detector that uses a machine learning facial categorization system to determine whether a person is wearing a mask or not, so that it may be connected to a CCTV system to verify that only persons wearing masks are allowed in. © 2021

关键词： COVID-19

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Area Efficient 0.009-mm2 28.1-ppm/°C 11.3-MHz ALL-MOS Relaxation Oscillator

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2024年第10期32卷 1900-1907页

作者： Wijaya, Joshua Adiel Chen, Poki Kumar Pradhan, Lucky Shahid Bhatti, Ahmad Kajihara, Seiji National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei106 Taiwan Kyushu Institute of Technology Fukuoka804-0015 Japan

This article presents an ultrasmall area on-chip relaxation oscillator with low-temperature sensitivity. In this design, a virtual resistor mainly composed of a complementary to absolute temperature (CTAT) voltage reference circuit is implemented to replace the real resistor for efficient temperature compensation, which counterbalances the inherent proportional to absolute temperature (PTAT) property of the original relaxation circuit of the oscillator. The conventional capacitor is also replaced with a MOS capacitor to complete the ALL-MOS oscillator circuit with two prime advantages, one of which is larger capacitance to area density, and the other is better matching with critical MOSFETs. Implemented in a 0.18-μm TSMC standard CMOS process, the proposed relaxation oscillator has achieved a temperature coefficient of 28.17 ppm/°C over the temperature range from -25°C to + 125°C at 11.39-MHz oscillation frequency. This circuit consumes 243.1μW under 1.3-V power supply. Along with the abovementioned excellent performance, the oscillator achieves an ultrasmall core chip area of 0.009 mm2, which is almost one order less than most of the prior arts' in the same process. © 1993-2012 IEEE.

关键词： Temperature

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Deep Learning Based Audio Assistive System for Visually Impaired People

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computers, Materials & Continua 2022年第4期71卷 1205-1219页

作者： S.Kiruthika Devi C.N.Subalalitha Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattankulathur603203India

Vision impairment is a latent problem that affects numerous people across the *** advancements,particularly the rise of computer processing abilities like Deep Learning(DL)models and emergence of wearables pave a way for assisting visually-impaired *** models developed earlier specifically for visually-impaired people work effectually on single object detection in unconstrained ***,in real-time scenarios,these systems are inconsistent in providing effective guidance for visually-impaired *** addition to object detection,extra information about the location of objects in the scene is essential for visually-impaired *** this in mind,the current research work presents an Efficient Object Detection Model with Audio Assistive System(EODM-AAS)using DL-based YOLO v3 model for visually-impaired *** aim of the research article is to construct a model that can provide a detailed description of the objects around visually-impaired *** presented model involves a DL-based YOLO v3 model for multi-label object ***,the presented model determines the position of object in the scene and finally generates an audio signal to notify the visually-impaired *** order to validate the detection performance of the presented method,a detailed simulation analysis was conducted on four *** simulation results established that the presented model produces effectual outcome over existing methods.

关键词： Deep learning visually impaired people object detection YOLO v3

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A bilateral assessment of human activity recognition using grid search based nonlinear multi-task least squares twin support vector machine

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Multimedia Tools and Applications 2024年第36期83卷 84121-84140页

作者： Thakur, Ujwala Prajapati, Amarjeet Vidyarthi, Ankit Department of Computer Science Engineering & amp Information Technology Jaypee Institute of Information Technology Noida Sector-62 Uttar Pradesh Noida201309 India

The recognition of individual activity has proven its importance in many application areas. Even after the pandemic crisis worldwide, the remote monitoring of human actions and their activities has increased a lot. In such situations, the smart sensors are utilized to capture the details remotely, share, and process intelligently using the computer-assisted machine. This paper introduces the new variant of the machine learning classifier, named Grid Search Based Nonlinear Multi-task Least Squares Twin Support Vector Machine (GMLT-SVM), to recognize human actions using sensory data. The proposed approach uses the grid search for hyperparameter tuning with a Nonlinear kernel for Multi-task classification in a twin support vector machine. This not only increases the efficacy of the model but also reduces the computation time for finding the optimal values of the hyper-parameters. The proposed approach when experimented with the sensory data of Human Action Recognition (HAR) gives an accuracy of 96.8%. The comparative analysis of the proposed method with existing other ML classifiers gives an increase in the accuracy of 94.2±2.6%. The experimental results suggest that the gained results are statistically significant enough to recognize human actions with sensory data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Goods Shipment Application Through Bids 10

Goods Shipment Application Through Bids

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Mahalakshmi, A. Saranya, E. Gomathi, B. Elizabeth, J. Ruby Abinaya, M. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Nehru Institute of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350384369

The practice of getting competitive quotes from carriers to convey goods is known as shipment bidding. Currently, open bidding is handled manually, and prospective bidders are frequently disqualified from taking part in the process. The system that is being proposed is designed to record every bidding procedure and make it cumulative in terms of monetary compensation. Real-time shipping tracking, feedback mechanisms, and bid evaluation tools are just a few of the features that the system offers. Shippers choose the best transportation company after weighing bids according to criteria including cost, level of service, and experience. In order to provide safe and effective financial operations, the proposed system also supports the payment and invoicing processes. Additionally, this application has analytical tools to produce reports and insights that help carriers cut costs. © 2024 IEEE.

关键词： Competition

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Evaluating the Efficacy of Machine Learning Methods in Phishing Detection: A Comparative Analysis 3

Evaluating the Efficacy of Machine Learning Methods in Phish...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Saraswathi, P. Keerthi, S. Reshmika, K.S. Gourav Pritham, G.R. Prabha, M. Dhiyanesh, B. Velammal College of Engineering and Technology Department of Information Technology Madurai India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

In the field of cybersecurity, the ongoing issue of phishing attacks is still of utmost concern because it puts both individuals and companies at serious risk. In order to prevent these fraudulent efforts and protect sensitive data, it is essential to implement effective detection and mitigation mechanisms. This research undertakes a thorough comparative evaluation of various machine learning models designed for phishing detection. Through a review of the literature on phishing detection techniques, we highlight the need for a methodical comparison analysis to identify the most effective strategies. In order to close this gap, we carefully assess the effectiveness of well-known machine learning methods, including Neural Networks, Gradient Boosting, XGBoost, Decision Trees, Random Forests, Support Vector Machines (SVM), and Logistic Regression. Using datasets from reliable sources, such as the PhishTank open-source service and the University of New Brunswick's dataset, we create a balanced dataset with 5000 authentic URLs and the same amount of phishing URLs. Our approach includes thorough feature extraction and preprocessing steps to guarantee the accuracy and applicability of the dataset. To evaluate each model's effectiveness, key performance indicators such F1-score, precision and recall are used. Our findings highlight subtle variations in the models' functionality and provide insight into their advantages and disadvantages for phishing detection. Additionally, we use visualisation approaches to explain each model's outputs, which offer deeper insights into the operational dynamics of each model. Our results reveal subtle differences in the performance of the models, explaining their various advantages and disadvantages in the field of phishing detection. The resulting comparative study provides useful information about the effectiveness of machine learning techniques, which helps decision-makers in practical deployment settings. © 2024 IEEE.

关键词： Phishing

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ASMA: An Advanced Secure Messaging Application

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Recent Advances in computer science and Communications 2022年第5期15卷 786-793页

作者： Gupta, Ankur Prabhat, Purnendu Sharma, Arun Department of Computer Science and Engineering Model Institute of Engineering and Technology Jammu India

Background: User-generated digital content, typically from mobile devices, can be of a personal nature. There have been several instances in which this personal content, including but not limited to, photographs, videos, messages and personal information, has been misused by the recipients. The misuse has ranged from violating privacy through unauthorized sharing to manipulating/modifying the original content and finally forgery and fraud. Objectives: The study aims to create an Advanced Secure Messaging Application (ASMA), allowing personal digital content to be shared across mobile devices in a secure, controlled and privacy-preserving manner. The application allows the user to explicitly specify a micro-policy to control the way digital content sent by the user, is consumed, shared or modified by the recipient(s). The users should be able to check the veracity of a shared news item besides controlling group formation and enforcing content sharing guidelines within a group. Methods: A micro-policy based novel mechanism is introduced for exercising fine-grained control over the manner in which digital content generated by users is shared and distributed among their contacts. Fact-checking is supported by cross-checking the message content against credible news sources. Results: Measurements of message throughput show a consistent and manageable overhead, due to a per-message micro-policy travelling with the message data compared to traditional message sharing applications. The high user-ratings received from users in a pilot test establish acceptance of the app and the need for its advanced features. Moreover, the overheads in implementing additional security features are imperceptible at the user level. Conclusion: ASMA is a highly secure and privacy-preserving messaging application, as demonstrated by experimental results and the pilot usability study. It offers novel security and content control features, which are not available in existing mobile messaging appl

关键词： Digital devices

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Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

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computers, Materials & Continua 2023年第1期74卷 2179-2194页

作者： R.Poonguzhali Sultan Ahmad P.Thiruvannamalai Sivasankar S.Anantha Babu Pranav Joshi Gyanendra Prasad Joshi Sung Won Kim Department of Computer Science and Engineering Periyar Maniammai Institute of Science and TechnologyThanjavur613403India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science and Engineering Jain Deemed to-be UniversityBangalore560069India Department of Neurology Annapurna Neuro HospitalKathmandu44600Nepal Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors(BT).A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival *** location and classification of BTs from huge medicinal images database,obtained from routine medical tasks with manual processes are a higher cost together in effort and *** automatic recognition,place,and classifier process was desired and *** study introduces anAutomatedDeepResidualU-Net Segmentation with Classification model(ADRU-SCM)for Brain Tumor *** presentedADRUSCM model majorly focuses on the segmentation and classification of *** accomplish this,the presented ADRU-SCM model involves wiener filtering(WF)based preprocessing to eradicate the noise that exists in *** addition,the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain ***,VGG-19 model is exploited as a feature ***,tunicate swarm optimization(TSO)with gated recurrent unit(GRU)model is applied as a classification model and the TSO algorithm effectually tunes *** performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.

关键词： Brain tumor diagnosis image classification biomedical images image segmentation deep learning

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