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Prediction of Cyberbullying Attacks on Twitter Data using ANN and NLP 3

Prediction of Cyberbullying Attacks on Twitter Data using AN...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Nixon, Israel Isravel, Deva Priya Punitha Malar Dhas, Julia Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381931

As cyberbullying has become more common, people are becoming more worried about the damage it may do to individuals and communities. This work aims to develop a model for predictive analysis that can scour Twitter data for patterns and foretell occurrences of cyberbullying. Therefore, a novel framework with a hybrid of ANN and NLP model is proposed to assess Twitter data. The proposed hybrid ANN-NLP model is fine-tuned by using the Vectorizer algorithm to detect and classify instances of cyberbullying more accurately. The efficiency of the proposed model is experimented on a dataset that has abusive or otherwise inappropriate language. The model exhibited better performance metrics in terms of precision, recall, f1-score and accuracy. The hybrid ANN-NLP model obtained an accuracy of 91% when compared with other classical methods. The results provide light on the features and patterns of cyberbullying and are useful for detecting crimes. © 2024 IEEE.

关键词： Neural networks

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Smart Surveillance Camera Using AI 2

Smart Surveillance Camera Using AI

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Manu, Y.M. Shashikala, S.V. Darshan, N. Dheeraj, A.P. Hemanth, N.J. Gowda, Nishanth B. Computer Science and Engineering BGS Institute of Technology Karnataka BG Nagar India

ISBN: (纸本)9798350383867

The Python script recognizes faces in real-time webcam video by using computer vision algorithms. For face detection, facial feature encoding, and face comparison with known faces, it makes use of the Open CV and face recognition packages. When the script detects a familiar face, it adds the name and the current time to a CSV file, indicating the person's attendance. In a similar manner, it records attendance for unidentified faces as "unknown." It also takes pictures of faces you don't recognize and saves them for later review or identification. The program repeatedly scans the video stream for faces, identifying them and cross-referencing them with a database of recognized faces. After a brief pause, if an unknown face is identified, a second face recognition procedure is started to either validate the identity or label the person as unknown. The script shows the promise of computer vision techniques in a variety of applications, such as security, identity systems, and attendance monitoring. It offers a straightforward but efficient approach for real-time face recognition and attendance tracking. © 2024 IEEE.

关键词： Video streaming

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Evolutionary Optimization for Biometric Feature Identification and Recognition

Evolutionary Optimization for Biometric Feature Identificati...

引用

2024 International Conference on Advances in computing Research on science Engineering and technology, ACROSET 2024

作者： Verma, Shraddha Bhanodia, Praveen Kumar Sethi, Kamal Kumar Rathore, Narendra Pal Singh Rajput, Shiv Shankar Mahajan, Anita Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Performance variability is a major difficulty for multimodal biometric systems, affecting their consistency and dependability in person identification. In order to solve this issue, the use of ensemble learning techniques is suggested in this work. The predictions of several classifiers are combined in ensemble learning to increase system consistency and performance as a whole. The unpredictability inherent in multimodal biometric systems is lessened by ensemble approaches, which integrate many classifiers trained on various subsets of data. By producing identification results that are more reliable and stable under many circumstances, this method improves system reliability. Improved accuracy and consistency can be achieved by integrating complementing data from many biometric modalities using ensemble learning techniques including bagging, boosting, and stacking. In practical applications, the suggested method shows promise in reducing performance variability and improving the dependability of multimodal biometric systems. © 2024 IEEE.

关键词： Contrastive Learning

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Predictive Modeling for Thread Optimization in OpenMP-Based Parallelization Using Machine Learning 17

Predictive Modeling for Thread Optimization in OpenMP-Based ...

引用

17th IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Yadav, Akash Ahmed, Mushtaq Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798331530471

High-performance applications frequently utilize parallelization tools to achieve faster execution times. OpenMP is a prominent library that facilitates the parallelization of sequentially written C code through directive-based parallelization. These directives employ various parameters to parallelize the code efficiently. Performance tuning of these parameters is typically performed manually, a method that is both time-consuming and suboptimal. Although several research efforts have sought to automate this tuning process, their effectiveness has been limited. Out of various parameters, deciding the number of threads to be executed in parallel is a crucial part. In this study, we first created a comprehensive dataset by executing loops containing different types of statements within their bodies. This dataset includes various parameters such as the number of threads, type of operations, loop trip count, and other relevant features. Then, we applied a Random Forest Regression-based machine learning technique to predict the optimal number of threads, i.e., indicates the ideal number of threads that maximizes performance, for parallelizing loops using OpenMP directives. The results indicate that our proposed approach significantly outperforms other regression-based methods, providing more accurate and efficient predictions for thread optimization. © 2024 IEEE.

关键词： Random forests

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Fed-Avis: A Lightweight Federated Learning Assisted Anti-Vehicle Infiltration System 24

Fed-Avis: A Lightweight Federated Learning Assisted Anti-Veh...

引用

25th International Conference on Distributed computing and Networking, ICDCN 2024

作者： Agrawal, Dewesh Singh, Gurpreet Singh, Harinderjit Singh, Kulvir Jha, Prianshu Patel, Yashwant Singh Computer Science & Engineering Thapar Institute of Engineering & Technology Punjab Patiala India

ISBN: (纸本)9798400716737

Vehicle anti-infiltration systems play a crucial role in modern security infrastructure, particularly in military security scenarios where unauthorized access poses a significant threat. However, current vehicle infiltration systems encounter with mobility, operational efficiency, and privacy-related challenges. Most of the conventional solutions often necessitate substantial processing power and complex hardware requirements. Thus, the integration of federated learning into anti-vehicle intrusion systems reduces the need for extensive computing resources while reducing operational costs and enhancing data accuracy and privacy. Hence, in this work, we introduce Fed-AVIS (Federated Learning-based Anti-Vehicle Infiltration System), which leverages motion sensors and camera traps in conjunction with lightweight deep learning models to reduce and simplify processing requirements. We apply the federated learning approach to a dataset comprising a total of 6,772 images, encompassing six distinct categories of military and civilian vehicles. To maintain system simplicity, we employ lightweight deep-learning models like TinyYOLOv4 and MobileNetV2. Additionally, to minimize latency, we utilize the Azure service bus queue for alert messages and location passing. The average message delay in Fed-AVIS is 6.5 seconds, with the longest delay observed at 11 seconds and the shortest at 4 seconds. Edge devices employed in the surveillance system, such as Raspberry Pi, exhibited power consumption ranging from approximately 2.84 to 4 watts when operating at maximum capacity. Overall the Fed-AVIS improves the performance in terms of computational cost, classification accuracy, energy saving, privacy, and communication latency comparing with the current approaches. © 2024 ACM.

关键词： Intrusion detection

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Attendance Management System using Haar Cascades and MTCNN Algorithm

Attendance Management System using Haar Cascades and MTCNN A...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Sharmila, G. Varalakshmi, I. Kaviya, M. Neha, K. Sherin, M.Juhe Suganya, P. Manakula Vinayagar Institute of Technology Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798331510022

A cloud-based attendance management system utilizing advanced facial recognition algorithms to enhance efficiency and accuracy in educational environments. By employing the CASIA WebFace dataset, which features a diverse collection of facial images, the system leverages deep learning techniques to extract meaningful features and identify individuals effectively. The process begins with image acquisition via webcams or mobile devices, followed by preprocessing to improve image quality. Face detection is accomplished using Haar Cascades, a swift method for identifying facial regions in real-time. Once detected, facial features are extracted using Convolutional Neural Networks (CNNs), which are designed to learn and classify complex patterns inherent in facial data. These extracted features are then compared against a centralized database, allowing for real-time updates to attendance records. The cloud infrastructure supports the processing of large datasets efficiently, ensuring rapid and accurate attendance tracking. This architecture enhances scalability, making it adaptable to various classroom sizes and environments. The findings demonstrate notable improvements in both accuracy and processing speed for attendance monitoring, highlighting the transformative potential of automated facial recognition systems in modern educational settings. This research contributes to the ongoing evolution of intelligent solutions, paving the way for streamlined attendance management practices. © 2024 IEEE.

关键词： Convolutional neural networks

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Innovative Solutions for Sustainable IoT in Smart City Infrastructure 5

Innovative Solutions for Sustainable IoT in Smart City Infra...

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Arul, S. Kavitha, P. Kamalakkannan, S. School of Computing Sciences Department of Computer Science Chennai Pallavaram India School of Computing Sciences Department of Computer Applications Chennai Pallavaram India School of Computing Sciences Department of Information Technology Chennai Pallavaram India

ISBN: (纸本)9798331504403

Smart cities are being created all over the world to enhance the safety and quality of life for their residents via the use of technology. Video surveillance, which includes placing cameras at key locations across the city to keep an eye on public areas and give law enforcement and other local authorities real-time access to recordings, is a crucial part of the architecture of smart cities. Cuttingedge technologies like deep learning, blockchain, edge computing, and cloud computing are used in modern video surveillance systems. This paper offers a comprehensive review of video surveillance systems for smart cities, examining both their benefits and drawbacks. In order to improve inhabitants' safety, security, and overall well-being, this article aims to show the benefits of video surveillance systems in smart cities and offers suggestions for their application. © 2024 IEEE.

关键词： Smart city

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A Dual-Layer Approach: Combining Lightweight and Dynamic RSA for Enhanced Data Security 5

A Dual-Layer Approach: Combining Lightweight and Dynamic RSA...

引用

5th IEEE International Conference on Communication, computing and Industry 6.0, C2I6 2024

作者： Balaji, R. Sriraam, C. Lionel Donato, L. Ragav, Krithin Kanthimathi, S. Vellore Institute of Technology School of Computer Science and Engineering TamilNadu Chennai India

ISBN: (纸本)9798350379884

An important aspect for both individuals and business data is security. Security has been studied extensively by many researchers. Security gaps or threats are ever increasing, and data protection is a primary issue. A lightweight hybrid cryptographic algorithm with two layers of encryption has been presented in this paper. A new effective, lightweight algorithm named ASCON-128 is used in the first layer, and a dynamic version of RSA (Rivest-Shamir-Adleman) is used in the second layer. Asymmetric and symmetric cryptographic features have been combined by this approach. The proposed algorithm's performance was evaluated using metrics such as execution time, throughput rate, delay, and latency. A higher level of security, a faster encryption execution times, reduced memory usages, and improved throughput were observed in the proposed algorithm when compared to widely used cryptographic systems. The resultsare shown by the experimental findings. © 2024 IEEE.

关键词： Encryption algorithms

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Bone Tumour Detection and Classification Using Deep Learning Based on Computed Tomography 2

Bone Tumour Detection and Classification Using Deep Learning...

引用

2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Rajath, S. Archana, R.A. Shivakumar, Kunaal Mathew, Kevin S Krishna, Mohit Bms Institute of Technology and Management Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

Bone Tumours may develop in any part of the bone and they usually occur in young adults or adolescents. Diagnosis requires at least a radiologist, surgeon, pathologist, and oncologist. For any suspected bone tumour lesion, radiography, CT, and magnetic resonance imaging are required. A groundbreaking initiative is underway in the realm of medical imaging, geared towards achieving early and accurate detection of bone tumours. By combining deep learning techniques to create an accessible diagnostic instrument for health care providers and patients. Three distinct deep learning models, including a basic Convolutional Neural Network (CNN), VGG-16, and DenseNet, will be implemented and trained for bone tumour classification. These models will leverage transfer learning to optimize their performance. The ultimate deliverable will be a user-friendly web platform built using Flask, offering a seamless interface for users to access and benefit from the trained models. This project is poised to make a significant impact in the realm of medical diagnostics, potentially revolutionizing bone tumour detection by facilitating early intervention and improving patient care. © 2024 IEEE.

关键词： computerized tomography

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Machine Learning-Based Fake News Detection System Using Blockchain 8th

Machine Learning-Based Fake News Detection System Using Bloc...

引用

8th International Conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2023

作者： Shwetha, A.N. Prabodh, C.P. Department of Computer Science Engineering Siddaganga Institute of Technology Karnataka Tumakuru India

ISBN: (纸本)9789819976218

Now a days, news on print and digital media has become very essential for everyone to stay aware with current happenings in the world. The increased amount of IT penetration and digitization has resulted in more number of people accessing various social media offerings like YouTube, Facebook, Twitter, Yahoo, etc. With the increased usage of social media, the amount of fake news also has increased disproportionately high. Fake news may cause economic and social problems. It is very difficult to find source of fake news and to make the concerned accountable for it. The accountability of authentic news is decreasing day by day. In this paper, we are proposing a Blockchain-based platform which acts as a media to share news. The source of news and each interaction will be recorded in the Blockchain and it is immutable. Users have read access to the news and they can also give rating to news based on its authenticity. Authors/journalists have write access, so that they can post their contents to media. Because all the interactions are available on Blockchain, it is easy to trace back the source of fake news if any. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Ethereum Fake news Logistic regression Machine learning

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