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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Singh, Akhilesh Kumar GLA University Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665487320

The use of online transactions in regular life has increased over the past ten years due to continuous development in technology and network connectivity. Online transactions are easy, simple, and user-friendly is the main reason for new users to constantly join the vast population benefitting them from the system. The main demerit of the online transaction system is a misuse of the system is defined as theft or one's credit card information which is used for self-profit without the permission of the cardholder. In this paper we have uses different ML algorithms such as regression, random forest classifier, and decision tree to control the heavily imbalanced dataset. Finally, in this research, we will calculate the accuracy, precision, recall, f1 score, and confusion matrix. © 2022 IEEE.

关键词： Decision trees

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Detecting Fine-Grained Emotions from COVID-19 Tweets Using Transformer-Based Architecture 9

Detecting Fine-Grained Emotions from COVID-19 Tweets Using T...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Kutty, Rida Javed Javed, Nazura Mallya, Rahul PES University Computer Science Engineering Bengaluru India St. Francis College Computer Science & Applications Bengaluru India

ISBN: (纸本)9798350306835

With the proliferation of social media, the task of identifying emotions in textual content has become increasingly relevant. Automating the task of detecting emotions from social media content has a wide range of applications today. In this work, we investigate the efficacy of pre-trained transformer models to detect fine-grained emotions from real-world tweets. We conduct experiments to fine-tune the DistilBERT and XLNet transformers to recognize nine emotions including neutral, anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Our DistilBERT and XLNet based models exhibit an F1-score of 0.6429 and 0.6679 respectively. The results are promising when compared with similar transformer-based models for fine-grained emotion recognition. We conclude from our study that fine-grained emotion detection from microblogs using transformers although promising is also challenging. The challenge is primarily on account of two reasons: a) difficulty in differentiating between intrinsically similar emotions and b) difficulty in building a large and balanced annotated corpus from real-world tweets. © 2023 IEEE.

关键词： Emotion Recognition

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Improved Resource Allocation in 6G Networks Through Artificial Intelligence 2

Improved Resource Allocation in 6G Networks Through Artifici...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Rolly Kumar Sagar, Pramod Malhotra, Sonal Kulshreshtha, Kushagra Srm Institute of Science and Technology Computer Science & Engineering UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Computer Engineering & Applications Up Mathura India

ISBN: (纸本)9798350371055

This paper proposes a unique resource allocation technique for 6G networks the use of synthetic intelligence (AI). To assist the evolution of cellular networks, aid allocation mechanisms must be designed to successfully allocate network assets and assist the large wide variety of to be had offerings. Thus far, this has been implemented with the aid of traditional algorithms which possess an upper restrict on their talents. AI-based useful resource allocation offers superior adaptively and scalability to such algorithms, through gaining knowledge of from historic records and feedback from the person terminals. On this paper, an improved aid allocation algorithm primarily based on the integration of AI technology is proposed, underneath the perspective of optimizing both electricity performance and network overall performance. The proposed set of rules relies on a deep Q-learning framework, taking gain from deep neural networks to dynamically examine the most reliable resource allocation strategy. Simulation consequences display an improvement in network performance and energy performance in comparison to traditional optimization schemes. © 2024 IEEE.

关键词： dynamically mechanisms propose schemes technique

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Recent Developments in Botnet Detection Tools and Techniques for IoT Security: A Review 2

Recent Developments in Botnet Detection Tools and Techniques...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Jain, Ruchi Nihalani, Neelu UIT-RGPV Department of Computer Science & Engineering M.P. Bhopal India UIT-RGPV Department of Computer Applications M.P. Bhopal India

ISBN: (纸本)9798350390179

The Internet of Things (IoT) embodies a paradigm shift in technology, connecting a diverse array of devices to enhance operational efficiency and user convenience across industries, educational settings, and urban infrastructures. This paper provides an in-depth examination of IoT's evolution, showcasing its profound impact on transforming traditional practices through interconnected smart systems. Despite its numerous advantages, the widespread adoption of IoT introduces significant challenges in cyber security and privacy. Recent high-profile cyber-attacks have underscored vulnerabilities inherent in IoT networks. This study delves into the growing threat of IoT botnets, sophisticated networks of compromised devices exploited by malicious actors for largescale attacks. It explores cutting-edge detection methodologies, including advanced deep learning models and architectural frameworks such as IoT-BDA, designed to proactively identify and mitigate these emerging threats. By critically analyzing current methodologies and technological innovations, this research aims to bolster awareness and develop robust strategies to safeguard IoT ecosystems. Ultimately, these efforts seek to establish a secure foundation that fosters continuous innovation and sustainable development within the IoT landscape. © 2024 IEEE.

关键词： Bot (Internet)

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Performance Analysis of Hybrid Cryptographic Algorithms in Serverless Platforms 1

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Raj, Vinay Suresh, Satharla Phridviraj, M.S.B. Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Kakatiya Institute of Technology & Science Warangal India

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

Serverless computing has become the buzzword in IT and it has been widely accepted because of its diverse cloud computing benefits. One of the major drawbacks of serverless computing is the security of the data which is handed over to the third parties. To ensure the security of the data and secure private communication between the services, hybrid cryptographic algorithms play a predominant role by combining the best security features of symmetric and asymmetric encryption techniques. In this work, two hybrid cryptographic algorithms are proposed namely 2-tier and 3-tier models and the performance of the two algorithms are analyzed in terms of encryption and decryption time along with the file size. The obtained results are also compared with an existing hybrid cryptographic model and it is observed from the results that the proposed hybrid model outperforms the existing model on both the 2-tier and 3-tier models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students 3

A Machine Learning Model for Predicting Internet Addiction i...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Johar, Geetika Patel, Ravindra University Institute of Technology Rgpv Dept. of Computer Science & Engineering Bhopal India University Institute of Technology Rgpv Dept. of Computer Applications Bhopal India

ISBN: (纸本)9798350384598

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student's learning performance, but also affects the student's physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features. © 2024 IEEE.

关键词： Contrastive Learning

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Network Media Platform to Build a Sustainable Development of Ecological Environment Using Blockchain Technology 3

Network Media Platform to Build a Sustainable Development of...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Sundar, G. Patchaiammal, P. Swetha, V.P. Rengarajan, Manikandan Satheesh, Nimmati Sasikala, P. Sindhi College Department of Computer Applications Chennai600077 India Panimalar Engineering College Department of MBA Chennai India RandD Institute of Science and Technology Vel Tech Rangarajan Dr. Sagunthala Avadi Chennai6000627 India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Department of Computer Science Bangalore India

ISBN: (纸本)9798350396157

Social technology has grown rapidly and effectively as a result of new information technology. The importance of the big data approach, which was among the most prominent current occurrences, has improved as a result of the amount of information generated from social media. For the installation of renewable energies, the Energy Network seems to have become a central feature. Because there are so many different sustainable energy sources and how they interact with the grid, the system is extremely complex and presents a number of intrinsic challenges, such as the coordination and monitoring of distributed sustainable energy generation, which can be extremely difficult to handle. This study presents a blockchain-based approach for managing digital information in a network as a solution to these problems. Blockchain is a distributed ledger that implements agreement, inevitability, and transparency for online data using encryption schemes, hash chains, and consensus mechanisms. The suggested approach can employ blockchain's features to achieve efficient network content creation management, copyright management, system administration, and user instructional techniques. Our suggested method can consistently provide the network media platform with crucial assistance, enabling it to pursue long-term, ecologically sustainable expansion. © 2024 IEEE.

关键词： Smart contract

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A holistic securing approach to speech steganography system with message embedding and pause optimization

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Multimedia Tools and applications 2025年 1-27页

作者： Kumar, Surendra Yadav, Arun Singh Dwivedi, Mridula Kumar, Mohit Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281401 India Department of Computer Science University of Lucknow Uttar Pradesh Lucknow226007 India Lucknow India Department of IT Dr. B.R. Ambedkar National Institute of Technology Jalandhar India

Nowadays, prevention control of message sharing as well as security enhancement in speech transmission which occurs due to users having a passion for novel inventions, communication system benefits. In this paper, the main objective is to design a speech steganography framework with covert message data transmission including speech. The secret message data is encrypted with the help of the Hyperelliptic Curve Cryptography (HECC) algorithm to provide a maximum security environment. The designed speech steganography framework is integrated with the pause removal mechanism respectively. Since, the Stegospeech is sent into the channel, as the Stegospeech as of now obtained the secret Message data its transfer speed will increment. Then at that point, it needs high bandwidth for transmission as well as needs higher energy utilization. In this manner, to decrease the data transfer speed, the best procedure is to eliminate the undesirable pause in the speech, the pause removal strategy carried out utilizing the combinations of Ensemble Empirical Mode Decomposition (EEMD) with Hilbert-Huang Transform (HHT). Then, some techniques are used to embed the message into the speech like 4-level Discrete Wavelet Transformation (DWT), Matrix definition, and Single Value Decomposition (SVD). They are likewise utilized for the secret message extraction from speech signal as well as message information from the Stegospeech signal. The presented strategy is carried out and analyzed towards the different invasions in the output. The Correlation Coefficient (CC), the Peak Signal-To-Noise Ratio (PSNR), the Signal-To-Noise Ratio (SNR), the Number of Pixels Changing Rate (NPCR), and the Unified Average Changing Intensity (UACI) are some of the popular standards that are utilised when comparing two encrypted voice signals. The numbers 33.3% and 100%, respectively, for UACI and NPCR are considered to be optimum states. It is noticed that the presented technique provides better Statistical resul

关键词： Steganography

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Impact of nutrients in food quality and safety by machine learning classifier using internet of things

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International Journal of Information Technology (Singapore) 2024年第5期16卷 2803-2812页

作者： Balamurugan, S. Gurumoorthi, E. Devi, P.P. Maruthamuthu, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp Department of IT CMR College of Engineering and Technology Telangana Hyderabad India Department of Computer Applications Madanapalle Institute of Technology and Science AP Madanapalle India

The traditional Food Supply Chain (FSC) is challenged by a number of issues, including uncertainty, security, expense, complexity, and quality worries. A precise supply chain is required to address these problems. Managing the supply chain to distribute high-quality goods is a difficult task in the modern food sector. The efficient Tree Augmented Naive Bayes linked with FSC utilizing Internet of Things (IoT) is proposed in this paper to enable tracking, tracing, and managing the full food supply chain process, including supplier, exporter, and consumers. This paper's goals are to evaluate the food safety and create the best possible chronological data for the analysis of potential future assumptions. It also intends to connect the producer and the buyer by delivering high-quality food goods while using IoT technology to transport food from the producers to the customers. IoT-based food supply chain traceability is used to successfully transact data with ambiguous, unreliable, and inadequate data. In order to provide secure backdrop and food safety for FSC process utilizing IoT technology, the suggested effective Tree Augmented Naive Bayes algorithm would be able to overcome the challenges of conventional supply chain. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Bayes classifier algorithm Food safety and IoT FSC Traceability Tree augmented Naive Bayes

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Heart disease Classification Utilising Machine Learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. Data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various Data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

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