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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Singh, Aditya Pratap Mishra, Santosh Kumar Nidhi Singh, Jagendra Agrawal, Madhup Joshi, Prakash Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Bennett University School of Computer Science Engineering & Technology Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350364569

In this paper, we investigated deep learning models for automated segmentation of liver and tumor regions from CT images to improve the diagnostic accu-racy as well as treatment planning in case of liver cancer. The research used a dataset of 3200 CT scans, with about 70% serving as the testing set and 30% for training. Preprocessing steps like Auto Windowing HU (Hounsfield Units) and Histogram Equalization to retain better clarity of the images. Based on the metrics of performance such as accuracy, Dice coefficient, IoU(Intersection over Union), precision and recall were calculated used three types of CNN models (ResUNet, VGG16 and VGG19). Its hybrid architecture combining U-Net and ResNet, called as ResUNet indicated better results with an accuracy of 97.3%, against VGG16 at 93.5% and VGG19 at 91.5%. The model also demonstrated good data loss reduction from 0.35 to about 0.10 over the course of a total for around an epoch angle in enduring caching past phase. VGG16 and VGG19 outperformed the baseline performances with reduced data losses while higher accuracies, although their convergence rate was slightly slower than ResUNet. The results confirmed the high precision of liver/tumor segmentation that ResUNet provides, which would be useful for clinical practice to achieve sufficient diagnostic improvement. The architecture of ResUNet specifically designed for the segmentation tasks in medical imaging from this study, demonstrates to be a versatile framework favoring its potential integration into automated tools within oncological diagnostics and could thus radically transform liver cancer detection. © 2024 IEEE.

关键词： computerized tomography

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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An Optimal Deep Learning for Cooperative Intelligent Transportation System

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computers, Materials & Continua 2022年第7期72卷 19-35页

作者： K.Lakshmi Srinivas Nagineni E.Laxmi Lydia A.Francis Saviour Devaraj Sachi Nandan Mohanty Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Periyar Maniammai Institute of Science&TechnologyThanjavur613403India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyTelangana500075India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Engineering College of Engineering PunePuneMaharashtra411005India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related *** the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS *** the same time,prediction of short-term traffic flow is highly essential to manage the traffic *** to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow *** paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so *** presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow *** IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count *** addition,deep belief network(DBN)model is applied for the prediction of short-term traffic *** further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of *** results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

关键词： Cooperative intelligent transportation systems traffic flow prediction deep belief network deep learning vehicle counting

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Investigations on Machine Learning Models for Mental Health Analysis and Prediction 2

Investigations on Machine Learning Models for Mental Health ...

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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Ajith Sankar, R. Juliet, Sujitha Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397635

Machine learning Techniques are identified as the most suitable methods for mental health analysis and prediction. Mental illness among people has increased vastly around the world and has become a serious human problem to be solved. From much research work and research articles, it is evident that machine learning algorithms can be an effective approach to finding mental illness. In this paper, different machine learning algorithms are investigated to find the best model, suitable to predict the mental health of a person more accurately and at a faster rate. In order to create a system that operates effectively and quickly, this paper investigates the performance of various machine learning models, including KNN, Support Vector Machine, Random Forest, Logistic regression, Decision tree, etc. All the models are compared based on the accuracy that each method offers after successful execution. © 2023 IEEE.

关键词： Forecasting

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Fusion of Feature Selection Techniques and Machine learning Algorithms for Attack Classification on 802.11 Wi-Fi AWID Dataset

Fusion of Feature Selection Techniques and Machine learning ...

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2023 IEEE Guwahati Subsection Conference, GCON 2023

作者： Jamil, Sofia Agarwal, Mayank Indian Institute of Technology Department of Computer Science & Engineering Patna801103 India

ISBN: (纸本)9798350337785

Vulnerabilities pertaining to the Media Access Control (MAC) layer are the primary reason for the existence of attacks in 802.11 Wi-Fi networks. In this paper, we aim to explore the Aegean Wi-Fi intrusion dataset (AWID) and design a model for detection and classification of network intrusions. There are three classes of attacks present in the dataset: impersonation, flooding, and injection. In intrusion detection systems (IDS), machine learning classifiers are one of the most effective solutions for preventing unauthorised access to resources. The key aspect of building a model based on machine learning is feature selection. In this paper, we have used four machine learning classifiers, namely Decision Tree, Random Forest, Gaussian NB, and Bernoulli Naive Bayes, for the classification of data as normal or intrusive. We have further applied filter methods and wrapper methods for feature selection. To the best of our knowledge, machine learning along with feature selection has never been used on the AWID-CLS-R dataset. The proposed work achieved the highest accuracy of 95.2% using a random forest classifier in 104 seconds using filter methods as the feature selection technique. In addition, we have used a confusion matrix and model building time for evaluating other machine-learning classifiers. © 2023 IEEE.

关键词： Wi-Fi

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Notes Sharing and Student performance Analysis Web Application 7

Notes Sharing and Student performance Analysis Web Applicati...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Swasha, K. Gracia, S. Steffie Maheswari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

Students frequently experience issues while taking notes, which interferes with their ability to study. There are many reasons students miss to take notes in college. Without notes, they might miss on important information and it would be really hard for the students during their exam times. Students will have a place to congregate and engage in collaborative learning through our application. It is possible for students to their materials (both written notes and documents in PPT, PDF, DOC, etc. format) for free access by other students in any branch or semester. Each user will log in to the application using their individual account. This application will also help the students to earn money. Students who wish to earn money while studying can lock their notes and the other students who want to download the notes have to pay the uploader. This way the application will benefit students to earn part time and also encourage students to take notes in class. There are few existing models for the said application but users face many difficulties like access to notes only after payment, does not accept hand written notes, must upload notes regularly, allows upload only for a particular stream. The proposed method provide solutions like students from different streams can upload their notes, notes can be downloaded for free or paid version based on how the uploader has set the download option. The other important feature is that Student Performance Analysis is done using Random Forest Regressor. The future work of this application is to enable student credit other students for their work by including credit system which will motivate the students to do a better job. This application is intended to be an Educate and Share application. © 2023 IEEE.

关键词： Students

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Importance of CURE Clustering Algorithm over K-Means Clustering Algorithm for Large Data-set 3

Importance of CURE Clustering Algorithm over K-Means Cluster...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Bharadwaj, Prakhar Gupta, Ragesh Gurjar, Ravi Singh, Anurag National Institute of Technology Delhi Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350300710

The method of finding natural groupings within multidimensional data based on some similarity metrics is known as data clustering. There are many clustering algorithms proposed over time, and many of them have gained considerable popularity due to the ease of use and efficiency of such algorithms. Although most of the algorithms perform well in many use cases, some are limited to specific types of data distributions. This paper aims to demonstrate the importance of Cure algorithm over the K-Means Algorithm with the help of a specified performance metric for large data sets. © 2023 IEEE.

关键词： K-means clustering

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SVM Algorithm based Fitness App Development with Age Classification using Andriod Studio 7

SVM Algorithm based Fitness App Development with Age Classif...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Flavya, L. Shreya, V. Sardar Maran, P. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

People's lives have been evolving due to the advancement of technology, especially in the aspect of mobile internet which has promoted the usage of fitness apps. With this app, users can exercise at anytime and anywhere. These fitness apps changed people's exercise and habits radically. With the help of this good exercise participation, people have gotten accustomed to making use of this fitness app as it has also brought additional attributes like advising on health tips which have made this app ever so special, which acts as an element of construction for obtaining the *** drastic improvement in the information gathering at online portals has helped fitness apps like springing to cope with the latest updates and help to act accordingly. Although public guidance has been crucial in decision-making, Studies show it is not favorable to follow those particular pieces of advice. Studies show that there are too many problems with fitness apps. A deficiency in time management, and no proper development, people do not refer to technical obstacles and relevant databases. The other major complication is that it is not able to keep up with the specification of different age groups. © 2023 IEEE.

关键词： Decision making

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Unveiling Threats: A Comprehensive Taxonomy of Attacks in In-Vehicle Networks 16

Unveiling Threats: A Comprehensive Taxonomy of Attacks in In...

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16th International Conference on Security of Information and Networks, SIN 2023

作者： Rai, Ritu Grover, Jyoti Pandey, Soumya Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350343212

Innovative automobiles are typically outfitted with numerous electrical and intelligent gadgets to offer users a better driving experience. Technological advancements have also raised severe safety and security issues despite their apparent advan-tages. This article summarizes the various attack surfaces of connected automobiles as they relate to technology advancements from the standpoint of the in-vehicle network. In particular, we described how attacks are launched through the physical interface using On-board Diagnostic (OBD-II) and USB ports and through the remote surface using various components such as ECU, sensors, infotainment system, Global Positioning Sys-tem (GPS), data centers, and cellular data. Given the importance of automobiles in terms of safety, more practical and effective efforts should be made within the connected vehicle network to secure linked vehicles and protect drivers and passengers. © 2023 IEEE.

关键词： Taxonomies

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Comparative analysis of detecting over-claim permissions from android apps

Comparative analysis of detecting over-claim permissions fro...

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Majethiya, Raj J Shah, Monika Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India

ISBN: (纸本)9798350399387

Android devices are popular nowadays due to their rich functionality and good performance. Android provides a permission model that supports user privacy by protecting restricted data access and actions on the Android operating system. Based on the app's defined functionality, any app must get users' permission to access different types of restricted data or resources. However, some developers request additional permissions other than the app's functionality, known as over-claim permissions. These over-claim permissions may steal sensitive information from users, Which is one of the leading privacy issues. Looking towards its criticality, many researchers have started working on detecting over-claim permission. This paper conducts a comprehensive survey of various approaches to identify and handle over-claim permissions. It also discusses the limitations of existing techniques and further research scope in this direction, which may help researchers define a path for designing new methods or optimizing existing strategies to resolve over-claim permission problems satisfactorily. © 2023 IEEE.

关键词： Android (operating system)

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