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4th International Conference for Emerging technology, INCET 2023

作者： Kumar, Raman Pati, Peeta Basa Deepa, K. Yanan, Suresh Amrita School of Computing Department of Computer Science and Engineering India Amrita School of Engineering Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Bengaluru India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350335750

Clustering is a method of grouping data based on similarities, and is an unsupervised technique for discovering patterns in data. In this research paper, various clustering algorithms such as k-Means, DBSCAN, Spectral Clustering, Gaussian Mixture, and Agglomerative Clustering are compared and evaluated on Amazon Prime Video Movies and TV Shows, Netflix Movies and TV Shows, and Disney+ Movies and Tv Shows datasets. The results of the study indicate that the k-Means algorithm performed well in clustering the data for all datasets, with an overall high level of performance. Additionally, the study provides valuable insights into the genre distribution of the data, and highlights the advantages and limitations of each clustering algorithm. © 2023 IEEE.

关键词： K-means clustering

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Blockchain-Based Enhanced Secure Data Storage Model: A Framework for the Internet of Vehicle Networks

Blockchain-Based Enhanced Secure Data Storage Model: A Frame...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Muhammad Umar Majigi Richard A. Ikuesan Ismaila Idris Shafi'i Muhammad Abdulhamid Cybersecurity Science Department School of ICT Federal University of Technology Minna Nigeria Department of Computing and Applied Technology College of Technological Innovation Zayed University Information Technology Department Community College of Qatar

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

The Internet of Vehicles (IoV) has gained significant attention due to its capability to facilitate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. However, IoV networks are highly susceptible to security and privacy threats, including unauthorized tracking of vehicle locations and Distributed Denial-of-Service (DDoS) attacks. To address these challenges, recent studies have explored blockchain technology as a potential solution for enhancing data security and privacy in IoV networks. This research proposes a blockchain-based enhanced secure data storage model for IoV networks, incorporating lightweight cryptography algorithms to ensure robust security. Additionally, the framework integrates statistical and machine learning models to analyze harvested vehicular data, providing predictive insights for threat detection and mitigation. The study presents a hypothetical V2V communication dataset comprising eleven attributes and four records representing a four-smart-vehicle scenario. Analysis of vehicular speed data reveals an average speed of 55 km/h, with maximum and minimum speeds of 100 km/h and 0 km/h, respectively. The total payload transmitted is 42 KB, with an average payload size of 10.5 KB. A key contribution of this study is the development of a Solana blockchain-based secure data storage model, where transactions are automated and validated through smart contracts. The model efficiently stores the large volume of data generated by smart vehicles and enables real-time data retrieval using embedded internet protocols. The proposed framework is expected to provide a highly secure IoV data storage system upon successful implementation while enhancing threat prediction accuracy and overall network security.

关键词： Support vector machines Analytical models Machine learning algorithms Accuracy Memory Predictive models Prediction algorithms Data models Blockchains Internet of Vehicles

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Cyberbullying Detection Using Deep Learning: A Comparative Study 2

Cyberbullying Detection Using Deep Learning: A Comparative S...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alkasassbeh, Mouhammd Almomani, Ammar Aldweesh, Amjad Al-Qerem, Ahmad Alauthman, Mohammad Nahar, Khalid Mago, Beenu Princess Sumaya University For Technology Computer Science Department Amman Jordan School Of Computing Skyline University College Sharjah United Arab Emirates AlHuson University College AlBalqa Applied University I.T. Department Irbid Jordan College Of Computing And It Shaqra University Shaqra Saudi Arabia Zarqa University Faculty Of Information Technology Computer Science Department Zarqa13110 Jordan University Of Petra Faculty Of Information Technology Department Of Information Security Amman Jordan Yarmouk University Faculty Of I.T Computer Science Department Irbid Jordan School Of Computing Skyline University College University City Of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350394962

Cyberbullying has become a serious issue with the rise in social media usage, especially among adolescents. Victims of cyberbullying often suffer from psychological distress, anxiety, depression and even suicidal tendencies. With the rapid growth of user-generated content online, manual detection of cyberbullying is extremely difficult. Hence, automated cyber-bullying detection systems are needed. In recent years, deep learning models have emerged as a promising technique for cyberbullying detection. This paper provides a comprehensive review of the use of deep learning for cyberbullying detection. The key contributions are: (1) a taxonomy of cyberbullying detection techniques, (2) a review of datasets used for model training and evaluation, (3) a comparative analysis of various deep learning models including CNN, RNN, LSTM, GRU and Transformer- based models, (4) a discussion of key challenges and limitations, and (5) suggestions for future research directions. The paper provides a critical perspective on model performance across multiple datasets. Key findings indicate RNNs and specifically LSTM and GRU models demonstrate state-of-the-art performance for sequential cyberbullying detection. The study concludes that deep ensemble models combining LSTMs and pretrained contextual embeddings show significant promise to advance the field. © 2024 IEEE.

关键词： Social networking (online)

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Deep Learning Approaches for Human Action Recognition in Video Data

arXiv

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arXiv 2024年

作者： Xie, Yufei College of Computing and Information Technologies National University Manila Philippines School of Information Engineering Jiangxi College of Applied Technology Ganzhou China

Human action recognition in videos is a critical task with significant implications for numerous applications, including surveillance, sports analytics, and healthcare. The challenge lies in creating models that are both precise in their recognition capabilities and efficient enough for practical use. This study conducts an in-depth analysis of various deep learning models to address this challenge. Utilizing a subset of the UCF101 Videos dataset, we focus on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Two-Stream ConvNets. The research reveals that while CNNs effectively capture spatial features and RNNs encode temporal sequences, Two-Stream ConvNets exhibit superior performance by integrating spatial and temporal dimensions. These insights are distilled from the evaluation metrics of accuracy, precision, recall, and F1-score. The results of this study underscore the potential of composite models in achieving robust human action recognition and suggest avenues for future research in optimizing these models for real-world *** Codes 68T45 © 2024, CC BY-NC-ND.

关键词： Recurrent neural networks

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Design and Implementation of an IoT-Based Smart Irrigation System for Optimized Water Management in Wheat Cultivation 12

Design and Implementation of an IoT-Based Smart Irrigation S...

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Shukla, Ravi Shankar George, Remya P. Hussain, Sajithunisa Ahmad, Nazia Babu, Prakash Jain, Vishal College of Computing and Informatics Saudi Electronic University Department of Computer Science Saudi Arabia The Applied College Imam Abdulrahman Bin Faisal University Computer Science Department Saudi Arabia Acharya Institute of Technology Dept of It Bangalore India

ISBN: (纸本)9798350369861

The current study presents the planning and implementation of a custom-built, Internet of Things (IoT)-based smart irrigation system for wheat cultivation that helps to combat problems such as water scarcity and inefficiency in traditional irrigation methods. The proposed device employs a network of environmental sensor that ensures optimal crop development with minimum water waste. These sensors monitor soil temperature and moisture levels and shows significant improvements in water management efficiency. When compared to standard irrigation approaches, the system's pilot installation results reveal considerable water usage reductions and benefits in the health of the wheat crop. These findings prove that the system is efficient at optimizing resource use and show how it can change the way wheat is farmed forever. © 2023 IEEE.

关键词： Internet of things

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Exploring the Dynamics of Exchange Rates: An Empirical Analysis and Predictive Modelling 2

Exploring the Dynamics of Exchange Rates: An Empirical Analy...

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

作者： Anand, R.N. Pati, Peeta Basa Deepa, K. Konidhala, Jothibabu K Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering India Amrita School of Engineering Department of Electrical & Electronics Engineering Bangalore India University of Technology and Applied Sciences-Al Musannah Department of Engineering Oman

ISBN: (纸本)9798350397635

In India, globalization and reforms in the financial sector have brought about a major overhaul of the financial landscape. The connection between financial markets and the tangible economy is now highly valued in the current scenario. Exchange rate affects a country's economy in several ways. It has a huge effect on international trade, economic growth, capital flows, interest rates and also inflation. Here, the aim is to understand the underlying factors that affects the exchange rate of US dollar with respect to Indian rupee. To accomplish this, a Vector Auto Regression (VAR) model, as well as other machine learning techniques such as multi-variable linear regression, polynomial regression, and regression tree, are employed to forecast exchange rates. Our findings indicate that Trade Balance is a significant factor which cause fluctuations in exchange rate of USD and regression tree model performs better from the side of all machine learning model applied for predicting the same. © 2023 IEEE.

关键词： Multiple linear regression

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Toward the Impossibility of Perfect Complete Quantum PKE from OWFs 16

Toward the Impossibility of Perfect Complete Quantum PKE fro...

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16th Innovations in Theoretical Computer Science Conference, ITCS 2025

作者： Li, Longcheng Li, Qian Li, Xingjian Liu, Qipeng State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China Tsinghua University Beijing China University of California La Jolla San DiegoCA United States

ISBN: (纸本)9783959773614

In this paper, we study the impossibility of constructing perfect complete quantum public key encryption (QPKE) from quantumly secure one-way functions (OWFs) in a black-box manner. We show that this problem is connected to a fundamental conjecture about the roots of low-degree polynomials on the Boolean hypercube. Informally, the conjecture asserts that for every nonconstant low-degree polynomial, there exists a universal (randomized) way to modify a small number of input bits such that, for every input string, the polynomial evaluated on the modified input string avoids 0 with sufficiently large probability (over the choice of how the input string is modified). Assuming this conjecture, we demonstrate the impossibility of constructing QPKE from quantumly secure one-way functions in a black-box manner, by employing the information-theoretical approach recently developed by Li, Li, Li, and Liu (CRYPTO’24). Towards resolving this conjecture, we provide various pieces of evidence supporting it and prove some special cases. In particular, we fully rule out perfect QPKE from OWFs when the key generation algorithm only makes a logarithmic number of quantum queries, improving the previous work, which can only handle classical queries. © Longcheng Li, Qian Li, Xingjian Li, and Qipeng Liu.

关键词： Boolean functions

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Revolutionising Role-Playing Games with ChatGPT

arXiv

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arXiv 2024年

作者： Stampfl, Rita Geyer, Barbara Deissl-O‘Meara, Marie Ivkic, Igor Department Information Technology University of Applied Sciences Burgenland Eisenstadt Austria Department Computing and Communications Lancaster University Lancaster United Kingdom

Digitalisation in education and its influence on teaching methods is the focus of this study, which examines the use of ChatGPT in a role-playing game used in the Cloud computing Engineering Master’s programme at the University of applied Sciences Burgenland. The aim of the study was to analyse the impact of AI-based simulations on students’ learning experience. Based on Vygotsky’s sociocultural theory, ChatGPT was used to give students a deeper understanding of strategic decision-making processes in simulated business scenarios. The methodological approach included role-playing and qualitative content analysis of 20 student reflections. The findings suggest that ChatGPT enhances students’ engagement, critical thinking, and communication skills, in addition to contributing to the effective application of theoretical knowledge. Furthermore, simulations can contribute to the effective application of theoretical knowledge. The results underscore the significance of adaptive teaching approaches in promoting digital literacy and equipping learners for the digital workplace. The integration of AI into curricula and the need for ongoing innovation in higher education are also emphasised as a means of guaranteeing excellent, future-focused instruction. The findings highlight the potential of AI and ChatGPT in particular, as an innovative cutting-edge educational tool that can both enhance the learning experience and help achieve the Sustainable Development Goals (SDGs) through education. © 2024, CC BY.

关键词： Students

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Sign to Speak: Real-time Recognition for Enhance Communication

Sign to Speak: Real-time Recognition for Enhance Communicati...

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applied Artificial Intelligence and computing (ICAAIC), International Conference on

作者： S Baghavathi Priya P V R Subba Rao TamilSelvi Madeswaran Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Information Technology University of Technology and Applied Sciences Nizwa Oman

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Recent developments in the area of research on sign language recognition have led to making use of deep learning techniques with the goal to assist the self-learning transition of sign language from one language to another. In order to detect and classify characters from photographs in multiple sign languages, a number of deep-learning models, have been deployed. SLR is a prominent area of study and technology that aims to mitigate speaking barriers for individuals who unable to talk and listen. This involves the development of tools capable of understanding and deciphering sign language movements, hence facilitating a wider range of interactions for those who are difficult of listening and speaking. This paper demonstrates a novel model called CNN Inception Net, which is a neural network based on convolution. The use of an Indian sign dataset enables the achievement of a notable performance rate of 98.9%, enabling precise recognition of actions in the language of signs.

关键词： Deep learning Sign language Accuracy Convolution Neural networks Assistive technologies Data augmentation

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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Computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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