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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thangavel, Gunasekaran Rajan, Sheeba Subbulekshmi, S. Anthonisamy, Arun Ganapathi, Rohini Velankanni, Gowri University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman SRM Institute of Science and Technology School of Computing Tiruchirapalli India Prathyusha Engineering College Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Business System Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Chennai India K. Ramakrishnan College of Technology Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9798331534950

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. Communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions. © 2024 IEEE.

关键词： Graphical user interfaces

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The Effect of Deep Learning Methods on Deepfake Audio Detection for Digital Investigation

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Procedia Computer Science 2023年 219卷 211-219页

作者： Mvelo Mcuba Avinash Singh Richard Adeyemi Ikuesan Hein Venter Department of Computer Science University of Pretoria Pretoria South Africa Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

Voice cloning methods have been used in a range of ways, from customized speech interfaces for marketing to video games. Current voice cloning systems are smart enough to learn speech characteristics from a few samples and produce perceptually unrecognizable speech. These systems pose new protection and privacy risks to voice-driven interfaces. Fake audio has been used for malicious purposes and is difficult to classify what is real and fake during a digital forensic investigation. This paper reviews the issue of deep-fake audio classification and evaluates the current methods of deep-fake audio detection for forensic investigation. Audio file features were extracted and visually presented using MFCC, Mel-spectrum, Chromagram, and spectrogram representations to further study the differences. Harnessing the different deep learning techniques from existing literature were compared using five iterative tests to determine the mean accuracy and the effects thereof. The results showed a Custom Architecture gave better results for the Chromagram, Spectrogram, and Me-Spectrum images and the VGG-16 architecture gave the best results for the MFCC image feature. This paper contributes to further assisting forensic investigators in differentiating between synthetic and real voices.

关键词： Deepfake audio digital investigation CNN voice cloning

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MANET Routing Protocol Performance Comparison: DSDV, AODV, and DSR 7

MANET Routing Protocol Performance Comparison: DSDV, AODV, a...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Choudhury, Tanupriya Kotecha, Ketan Graphic Era Hill University School of Computing Dehradun India GLA University Department of Computer Engineering & Applications UP Mathura India Maharashtra Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Maharashtra Pune412115 India

ISBN: (纸本)9798350383065

Mobile Ad hoc Networks (MANETs) may self-configure and rely on wireless connections rather than a permanent infrastructure;hence this research delves into their intricacies. This research aims to better understand how MANETs function. MANETs are characterized by their high degree of dynamism and adaptability to changes in network architecture. These networks are made up of mobile nodes that are independent and can perform the functions of both a host and a router. Having said that, this flexibility does provide a number of substantial issues, notably in the area of routing. The unexpected and fluid nature of MANET topology, which is related to the mobility of the nodes, makes routing a hard process. The effectiveness of the network is strongly reliant on the efficacy of the routing protocols that are implemented;therefore this is an important consideration. Our study analyses the capabilities of three popular MANET routing protocols: DSDV, AODV, and DSR. These protocols must be compared using end-to-end latency, packet delivery percentage, and lost packet ratio across different network densities and mobility patterns. In our simulations, we seek to replicate a wide variety of situations that can occur in the real world by altering the number of nodes, the connection counts, and the pause periods. These simulations are carried out with the help of the Network Simulator 2 (version 2.35), a programme that is well-known for its use in network research. Additionally, the graphical representation of the data is carried out with the assistance of gnuplot. Our first findings suggest that AODV has a significant advantage over DSDV and DSR when it comes to handling the dynamic networking environment of MANETs more effectively. This is especially true in situations characterised by high node mobility and variable network density. This research not only highlights the necessity of selecting the appropriate routing protocol in MANETs, but it also offers a thorough framework for

关键词： Routing protocols

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Flight Delay Prediction Using Machine Learning 8

Flight Delay Prediction Using Machine Learning

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Reddy, Ravula Tarun Basa Pati, Peeta Deepa, K. Sangeetha, S. Tresa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India University of Technology and Applied Sciences Department of Engineering Al Musannah Oman

ISBN: (纸本)9798350334012

The prediction of flight delay can be considered as one of the most challenging problems to solve. Delay of an aircraft is not only a problem for an airline but also for the passengers. Flights can be delayed due to several reasons, the weather being the primary one. In this paper, our focus is to predict the delay of the flights due to bad weather. The dataset used consists of flight data from JFK airport from the sources Bureau of Transportation statistics and weather data from the National Centers for Environmental Information. The results of different machine learning algorithms like Linear Regression, SVR, Decision Tree Regressor, Random Forest Regressor, Ridge, and Lasso Regressor, for prediction of flight delay, are compared. XGboost regressor had the best performance in all the scenarios with least RMSE score of 0.81. © 2023 IEEE.

关键词： Decision trees

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Learning Self-Supervised Vision Transformers from Scratch for Aerial Person Re- Identification 13

Learning Self-Supervised Vision Transformers from Scratch fo...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Peng, Houfu Xu, Lili Lu, Xing Xia, Daoxun Guizhou Normal University Guizhou Key Laboratory of Information and Computing Science Guiyang China School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

In recent years, person re-identification (Re-ID) as a widely studied computer vision task, has reached a saturation state under closed-world setting, which encourages researchers to further explore more realistic scenarios. Among them, person Re- Idin aerial imagery is proposed and improved due to its unique practical importance in public security. However, since the aerial person images are taken by unmanned aerial vehicles (UAV s), influenced by camera height and angle of view, there are more serious problems such as weak appearance feature and occlusion than ground person images. Most of the current state-of-the-art person Re-ID methods on closed-world datasets are based on local convolution neural network, and hardly works well when applying them to aerial person Re- Idtasks directly. In this paper, we improve the emerging vision transformer (ViT) and apply it to the person Re- Idin aerial imagery. It should be noted that a large amount of data is required to be pretrained for ViTs to achieve competitive performance. Considering the limitations of data, computing power and flexibility in practical scenarios, we improve the pre-training process based on self-supervised learning, and achieve training ViTs from scratch with limited data. Specifically, in pre-training stage, the self-supervised paradigm based on parameter instance discrimination is applied to capture feature alignment and instance similarity, which alleviates the data-hungry of ViTs caused by the lack of inductive bias. Extensive comparative evaluation experiments are conducted on the aerial Re- Iddataset. Our method achieves a Rank-1 accuracy of 65.29% and a mean average precision (mAP) of 57.31%, which proves its effectiveness in aerial person Re-ID tasks. © 2023 IEEE.

关键词： Antennas

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Real-time Student Classroom Feedback with the RFMNet Framework in a Smart Classroom 13

Real-time Student Classroom Feedback with the RFMNet Framewo...

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13th International Conference on Information technology in Medicine and Education, ITME 2023

作者： Tang, Xianpiao Hu, Pengyun Yu, Dewei Xia, Daoxun School of Big Data and Computer Science Guizhou Normal University Guiyang China Guizhou Normal University Guizhou Key Laboratory of Information and Computing Science Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350319156

Facial expression recognition is the ability to interpret human feelings from facial features. Teachers can use it to visualize the state of emotion and estimate students' learning effects to take corresponding intervention strategies to improve teaching. The current technology has shortcomings such as network performance degradation and feature information loss, which cannot meet the needs of sentiment analysis of student feedback. Therefore, RFMNet, a novel network model based on deep learning theory, is proposed in this paper. Featuring higher extraction ability, it is conducive to accurately analysing students' expressions. First of all, this model introduces a relation-aware global attention (RGA) module to better integrate the more discriminative expression features in the image and achieve more refined sentiment analysis. Second, the Mish activation function and the focal loss function are employed. The Mish activation function is introduced to avoid disappearing feature information due to neuron deactivation when the ReLU gradient is 0. The model achieves an average recognition accuracy of 89.62%, as confirmed through a sequence of trials conducted on the public dataset FERPlus. The model helps achieve intelligent processing of teaching information and obtaining real-time feedback by detecting, extracting, and analysing the students' facial expressions. © 2023 IEEE.

关键词： Students

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Motion Capturing in cricket with bare minimum hardware and optimised software: A comparison of MediaPipe and OpenPose 1

Motion Capturing in cricket with bare minimum hardware and o...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Jayaweerage, Ishan Rajamanthri, Kanishka Kothalawala, Avishka Gunawardana, Niroshan Induranga, Ashan Weerakkody, Pathum Maduwantha, Kaveendra Kumara, Samantha Koswattage, Kaveenga Computing Sabaragamuwa University of Sri Lanka Belihuloya70140 Sri Lanka Sabaragamuwa University of Sri Lanka Physical Education Department Belihuloya70140 Sri Lanka Technology Sabaragamuwa University of Sri Lanka Belihuloya70140 Sri Lanka Sabaragamuwa University of Sri Lanka Applied Sciences Belihuloya70140 Sri Lanka

ISBN: (纸本)9798350352931

In sports, wearable systems and optical systems are used to estimate human poses and calculate body angles using key points. Wearable systems are not suitable for use in an outdoor environment since they disturb to players. The optical methods such as MediaPipe (Google Inc.) and OpenPose (CMU) are used to capture the humans using key points. Optical approaches can be used with daylight on an outdoor background without a controlled environment. This study compares the MediaPipe and OpenPose with the HMDB51 dataset, UCF Sports Action dataset, and video footage captured by the team from a cricket player. The result of this study identified that MediaPipe is better than OpenPose in capturing humans for abstracting the key points accurately to analyze the data. MediaPipe can be suggested as the best approach to capture the human skeleton from the motion withing low performance devices like using low cost camera(s) with a CPU instead of a GPU. © 2024 IEEE.

关键词： Cricket (Sports)

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FEATURE-pHLA: Physico-chemical features efficiently predict peptide-HLA binding affinity

FEATURE-pHLA: Physico-chemical features efficiently predict ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hamda Alhosani Raghvendra Mall Ankita Singh Filippo Castiglione Biotechnology Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates Institute for Applied Computing National Research Council of Italy Rome Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Human leukocyte antigen or HLA plays a crucial role in the recognition of antigenic peptides as this binding is responsible for subsequent immune response by eliciting T-cell activation. Accurate prediction of peptide-HLA binding affinity is imperative for facilitating vaccine development and immunotherapies. Recent advancements in transformer-based models and protein language models in predicting peptide-HLA interactions have shown significant improvements. Current methodologies rely on deep learning methods and GPU-intensive computations. We propose a simple and computationally cheaper method that demonstrates efficacy. Our tree-based model, named FEATUREPHLA, utilizes the physico-chemical fingerprints obtained from peptides and HLA sequences and is highly *** goal was to estimate the predictive efficacy of these physico-chemical features for the task of peptide-HLA binding prediction. Our proposed method outperforms other methods on experimentally verified peptide-HLA binders from the HPV vaccine data securing the highest number of true positives and the lowest number of false negatives, thereby, showcasing its predictive power on real-world scenarios. Our study reveals the relevance of biology-inspired features for the calculation of molecular interactions and lays the groundwork towards developing more accurate biology-informed predictive models.

关键词： Proteins Protein engineering Accuracy Computational modeling Peptides Lymphocytes Predictive models Transformers Biology Vaccines

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Multi-Keyword Graded Exploration in Encrypted Cloud Data for Industries Based on Rc4+ and Forest

Multi-Keyword Graded Exploration in Encrypted Cloud Data for...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： P. Balamurugan Suresh Thangakrishnan. M G. Arulkumaran Santhosh Jayagopalan Dept. of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India CSE Einstein college of Engineering School of Computing and Information Technology REVA University Bengaluru Department of Computing British Applied College Umm Al Quwain UAE

The vast amounts of storage space offered by the cloud encourage businesses to store their most sensitive files and data on remote servers accessible over the internet. When comparing a local server and personal computer to a cloud environment, it's clear that many more tasks can be accomplished with the former. Cost, speed, global scalability, productivity, and performance are just a few of the six reasons why cloud services have gained popularity among businesses and consumers alike in recent years. This paper presents the River cypher RC4+ (RCPF) scheme for fast, efficient, and secure multi-keyword search indexing in an encrypted cloud environment. In order to encrypt and decrypt data, the RC4+ algorithm uses a final symmetric secret key that is created by a pseudorandom byte generator. In order to generate an index list for each file in a shorter amount of time, a minimal spanning tree is formed. After that, the encrypted index list is searched by the cloud server, and relevant files are ranked according to the search query. The user's decryption of the encrypted data using the symmetric key behaves as a whole.

关键词： Cloud computing Technological innovation Heuristic algorithms Throughput Generators Encryption Cryptography

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Churn Rate Prediction System using Logistic Regression, SVM, and Random Forest Models 2

Churn Rate Prediction System using Logistic Regression, SVM,...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Jain, Paras Gupta, Vishan Kumar Pandey, Priyank Anupriya Ghai, Kapil Singh, Ajay Vit Bhopal University School of Computing Engineering and Artificial Intelligence Kothri Kalan MP Bhopal India Amity University Punjab Amity School of Engineering and Technology Punjab Mohali India Graphics Era Deemed to be University Department of Cse Dehradun India Graphic Era Hill University School of Computing Dehradun India Graphic Era Hill University Department of Chemistry Dehradun India Uttaranchal University School of Applied and Life Sciences Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350388916

Customers are the life blood of any business, and their importance cannot be overstated. Customers are a business's primary source of revenue. Retaining and gaining new clients is crucial to profitability. Over time, repeat customers typically spend more, and gaining new customers is frequently more expensive than keeping current ones. Consequently, establishing a foundation of devoted clients might boost revenue. Without customers purchasing products or services, there would be no income to sustain the business operations or drive growth. It might have several detrimental implications, such as a decline in market share, loss of revenue, bad impact on staff morale, etc. To tackle this problem supervised learning algorithms are trained on historical data, where the input features and corresponding churn labels are known. The goal is to learn patterns from the data that can help and predict churn accurately for newly unseen data. These algorithms guarantee a high degree of accuracy when forecasting customer attrition, allowing businesses to precisely identify consumers who pose a danger. Businesses frequently use focused tactics, such as re-engagement campaigns with discounts or offers that are specifically catered to the requirements and interests of each churned consumer, to entice and re-engage these customers through individualized communication. © 2024 IEEE.

关键词： Random forests

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