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Cyberbullying Detection on Social Media using a Hybrid Machine Learning Model

Cyberbullying Detection on Social Media using a Hybrid Machi...

引用

Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Priyadharsini. P Geetha. R Dept. of Computer Science & Engineering S. A. Engineering College Chennai India Head Dept of Computer Science & Engineering S. A. Engineering College Chennai India

ISBN: (数字)9798331598488

ISBN: (纸本)9798331598495

Cyberbullying has increasingly become a significant issue in digital environments, Causing psychological harm among users. Effectively identifying harmful content is difficult due to the complexity of language and the constantly evolving slang. This research tackles the shortcomings of conventional detection techniques by incorporating capsule networks with BERT-based Transformers to enhance contextual comprehension. The proposed hybrid model captures hierarchical connections and deep semantic features to boost classification accuracy. Experimental results indicate that the model achieved 99.0%of accuracy, 97.83% precision, 80.0% of recall, and f1-score 98.9% surpassing existing methods. Future improvements will be involve expanding the dataset and fine-tuning sentiment analysis methods for excellent adaptability.

关键词： Support vector machines Sentiment analysis Adaptation models Analytical models Accuracy Text categorization Semantics Cyberbullying Psychology Transformers

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COPAR: Coordinated Packet Rerouting Attacks in NoC 12

COPAR: Coordinated Packet Rerouting Attacks in NoC

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12th International Conference on Intelligent systems and Embedded Design, ISED 2024

作者： Saritha, Sreehari Udayakumar Rajan, Manju Jose, John Govt. Engg. College Barton Hill Dept. of Information Technology Kerala India Indian Institute of Technology Guwahati Dept. of Computer Science & Engineering Assam India

ISBN: (纸本)9798331522612

In modern system-on-Chips (SoCs), the reliance on third - party intellectual property (IP) to meet demanding time-to-market and cost constraints introduces significant security vulnerabilities, particularly through the insertion of Hardware Trojans (HTs). These malicious circuits can severely compromise the performance and reliability of Tiled Chip Multicore Processors (TCMPs), which utilize Network-on-Chip (NoC) architectures for efficient inter-core communication. This paper presents a coordinated HT model that coordinates a packet rerouting attack within an 8×8 mesh NoC. By embedding HTs within the input units of multiple routers, the attack dynamically alters the destination addresses of packets, causing them to take unnecessary and indirect routes through the network. This malicious rerouting results in significant delays, leading to increased average packet latency, reduced instructions per cycle (IPC), and decreased overall throughput. Experimental results demonstrate the effect of this coordinated HT attack, increasing the packet latency by 2.33x, increasing the link utilization by 1.74x and decreasing the instructions per cycle by 45 %, degrading N oC performance. © 2024 IEEE.

关键词： DoS Hardware Trojan (HT) Network-on-Chip (NoC) Security system-on-Chip (SoC)

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Attention Mechanism with Bayesian Decision Theory for Drug Prediction

Attention Mechanism with Bayesian Decision Theory for Drug P...

引用

IEEE International Conference on system, Computation, Automation and Networking (ICSCAN)

作者： A. Sankaran K. Sathiyamurthy Dept. of Computer Science and Engineering Puducherry Technological University India Faculty of Computer Science and Engineering Puducherry Technological University India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

Biomedical literature lacks integration of dynamic drug interaction data for personalized prescriptions. This proposed work is a new hybrid model that enhances drug safety prescriptions by taking into account individual patient features and drug interactions. It does this by hybrid attention mechanisms with Bayesian Decision Theory (BDT), which employs Bayesian statistical thinking instead of the dot-product approach. This approach is driven by the growing complexity of patient profiles and the limitations of current algorithms, which often fail to dynamically change the relevance of individual aspects. This method seeks 94% accuracy, effectively prioritizing safer drug combinations and personalizing drug recommendations. This integration leads to more consistent and customized drug prescriptions, which ultimately improve patient outcomes and advance the field of personalized healthcare.

关键词： Drugs Accuracy Attention mechanisms Uncertainty Computational modeling Decision theory Medical services Predictive models Bayes methods Safety

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Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms 4

Improving Existing VANETs by Incorporating UAVS Using Ant Co...

引用

4th IEEE International Conference on Data engineering and Communication systems, ICDECS 2024

作者： Thangam, S. Dixit, Aniket Karn, Alok Kumar Thakur, Arnav Arunbalaji, Cg Jesy Janet Kumari, J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350393354

Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion;despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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An Overview of Virtual Reality 31st

An Overview of Virtual Reality

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31st International Conference on Software engineering and Data engineering, SEDE 2022

作者： Lewis, Christopher Harris, Frederick C. Dept. of Computer Science and Engineering University of Nevada Reno RenoNV89557 United States

Virtual Reality (VR) has existed for many years;however, it has only recently gained wide spread popularity and commercial use. This change comes from the innovations in head mounted displays (HMDs) and from the work of many software engineers making quality user experiences (UX). In this work we present a brief history, current research areas, and areas for improvement in virtual reality © 2022, EasyChair. All rights reserved.

关键词： Virtual reality

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Machine Learning-Driven Prediction of Gleason Score 7 Prostate Cancer Patterns Using Multi-Omics Data

Machine Learning-Driven Prediction of Gleason Score 7 Prosta...

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International Conference on New Trends in Computing sciences (ICTCS)

作者： Santosh Venkatraman Aniruddha Chakravarty Nethan Shaik Ibrahim Al-Hurani Abedalrhman Alkhateeb Dept. of Computer Science Lakehead University Thunder Bay ON Canada Dept. of Electrical and Computer Engineering Lakehead University Thunder Bay ON Canada

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

This study aims to develop a novel advanced Prostate Cancer (PCa) prediction system that utilizes vital omics (DNA Methylation, Gene Expression, and Copy Number Alteration) combined with gene-specific mutation features to stratify 3 + 4 and 4 + 3 Gleason Score samples. Although the 3 + 4 and 4 + 3 samples are variants of grade-7 PCa, the latter is more severe. Histopathological and clinical similarities between these classes often lead oncologists to misdiagnose them. The utilized dataset in this study contains the aforementioned omics data for the most mutating genes (including SPOP, FOXA1, DPYSL2) with Gleason Score as the target, combined with the mutation profiles for 11,690 genes. This study used a Conditional Tabular Generative Adversarial Network (CTGAN) for handling class imbalance resulting in 145 samples in each class. This dataset was subjected to standard scaling and binary classification using GridSearchCV tuned models such as XGBoost. Mutation features from highly mutating genes were then combined, followed by dimensionality reduction using PCA and a shallow Autoencoder. Binary classification from here on was done with custom deep ANN classifiers in addition to the hyperparameter tuned ML models. An alternate approach to achieve more accuracy was followed where a deep Autoencoder was used to obtain 13 latent features from the dataset. Recursive Feature Elimination (RFE) in combination with a Random Forest estimator, were used to obtain the highest-ranking latent features. Isolation Forest and One-Class Support Vector Machine (O-SVM) were then used to effectively stratify the samples. In addition, Kullback Liebler Divergence (KLD), a probability and information loss-based approach was used to study whether all the 3 + 4 and 4 + 3 samples varied significantly. XGBoost achieved the best accuracy (66–71%), outperforming ANN-4 (66%), Random Forest (62%), and a Max-Vote classifier (61%). The system effectively integrates multi-omics data and gene mutations,

关键词： Support vector machines Accuracy Autoencoders Feature extraction Market research Generative adversarial networks Prostate cancer Random forests Standards Principal component analysis

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Signals of Opportunity for Car Density Estimation with Limited Training Data

Signals of Opportunity for Car Density Estimation with Limit...

引用

2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Al Amiri, Wesam Abdelsalam, Omar Jones, James T. Guo, Terry N. Mackenzie, Allen B. Tennessee Technological University Dept. of Electrical and Computer Engineering CookevilleTN United States Tennessee Technological University Dept. of Computer Science CookevilleTN United States Center for Manufacturing Research Tennessee Technological University CookevilleTN United States

ISBN: (纸本)9798350335590

Passive sensing leverages existing signals from illuminators of opportunity to perform target localization, detection, and tracking without the need for additional infrastructure. In this paper, we explore the use of WiFi signals of opportunity for estimating car density in parking lots. The aim is to develop an efficient and cost-effective parking occupancy estimation system to alleviate traffic congestion caused by drivers searching for parking spaces. To achieve this, experimental measurements were conducted in a real outdoor environment to collect reflected WiFi signals from targets (cars). These collected signals were then used for car density estimation using a combination of semi-supervised learning convolutional neural network (CNN) and weighted-centroid interpolation techniques, only requiring small size datasets and limited measurements. The proposed method overcomes the limitations of existing data-driven estimators by reducing the reliance on large labeled datasets and computational complexity associated with traditional supervised learning methods. In addition, it provides a cost-effective alternative to the traditional systems that rely on a large number of sensors. Simulations are performed to evaluate the performance of the estimation, and the results demonstrate that our scheme can effectively estimate car densities in the parking lot with reasonable estimation errors. © 2023 IEEE.

关键词： Convolutional neural networks

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A Novel Approach for the Detection of Tea Leaf Disease Using Deep Neural Network

A Novel Approach for the Detection of Tea Leaf Disease Using...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Datta, Saikat Gupta, Nitin Dept. of Computer Science and Engineering National Institute of Technology Hamirpur HP Hamirpur177005 India

India is among the biggest tea exporter in the world. However, tea leaf diseases caused by persistent pathogen exposure result in considerable crop yield losses around the world. Detection of the disease of tea leaves at early stages can reduce the damage of tea output. Detecting the disease with the naked eye can be inefficient and counterproductive. Convolutional Neural Networks (CNNs) are commonly used to implement an effective method for the image classification. In detection of plant disease, the use of CNN is widespread. Therefore, in the proposed work, a Deep CNN having multiple hidden layers is considered for the classification of diseased tea leaves into different categories. This helps the network in detecting more number of features and thereby improving the accuracy in disease detection. The classification is done consisting of the following categories of leaves;Gray Blight, Algal Spot, Brown Blight, Helopeltis, Healthy Leaves and Red Spot. Further, a labeled dataset consisting of 5867 diseased and healthy tea leaf images have been created and uploaded on Kaggle. The suggested method demonstrates that the model is able to accurately detect the kind of persistent tea leaf disease with a 96.56% accuracy. The accuracy of the following disease classes are as follows, Algal Spot has an accuracy of 98.23%, Brown Blight has an accuracy of 97.98%, Gray Blight has an accuracy of 93.46%, Healthy classes of leaves has an accuracy of 99.10%, the Helopeltis disease class has an accuracy of 98.98% and Red Spot has an accuracy of 92% The model that is proposed in this literature is far superior than the existing methods in terms of accuracy. Furthermore this model can be adopted to work with various IoT devices to deploy in real world applications and the architecture of this model can be used to train different crop images to classify their diseases. © 2023 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Image Degradation due to Interacting Hot Pixels and SEUs 37

Image Degradation due to Interacting Hot Pixels and SEUs

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37th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology systems, DFT 2024

作者： Chapman, Glenn H. Farahmandpour, Alireza Chakma, Amit Koren, Israel Koren, Zahava School of Engineering Science Simon Fraser University BurnabyBCV5A 1S6 Canada University of Massachusetts Dept. of Electrical and Computer Engineering AmherstMA01003 United States

ISBN: (纸本)9798350366884

Hot pixels (induced by cosmic rays) in digital imaging sensors accumulate with camera age, impacting the quality of all images produced by the camera. During its lifetime, the imager also experiences Single Event Upsets (SEU s) that create transient defects affecting a single image. The SEUs occur at a much higher rate (events/second) compared to 10-100s additional permanent hot pixels that occur per year. We explore in this paper how an SEU that occurs within a close proximity (within a 5×5 pixel area) of an existing Hot Pixel may distort the image. We also consider situations where an SEU impacts two or more adjacent pixels. When such events happen, the currently employed color demosaicing and JPEG image compression algorithms spread the damage into a 16×16 pixel area, creating significant color changes and resulting in noticeable image degradation. We use formulas developed for a two Hot Pixel interaction to estimate the probability of a Hot Pixel-SEU interaction, and predict its increase with camera age. © 2024 IEEE.

关键词： Cosmic rays

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Implementing Secure Key Management and Distribution in Cloud Environment 2

Implementing Secure Key Management and Distribution in Cloud...

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2nd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2023

作者： Lamsal, Aman Paul, Aniket Buchade, Amar Nalavade, Jagannath Bidwe, Ranjeet MIT Art Design and Technology University Dept. of Computer Science and Engineering Pune India Vishwakarma Institute of Information Technology Pune Savitribai Phule Pune University Pune India Department of Computer Engineering Pune India

ISBN: (纸本)9798350333763

Key management is considered as an essential aspect of securing cloud-based systems and data. As more organizations move their operations to cloud, the need for secure and efficient key management solutions becomes increasingly important. En-suring security of cloud computing is essential as many users often store their sensitive information with the cloud providers. Cloud service providers offer Key Management Services (KMS) to manage cryptographic keys for their customers, but there are still several challenges related to key management in cloud environments, including key storage, access control, and key rotation. Our research project aims to address these challenges by developing a secure and efficient key management solution for cloud-based systems. We propose a system that uses a combination of various cloud services and encryption algorithms to generate, store, and manage cryptographic keys securely. Our system provides granular access controls, and strong encryption to ensure the confidentiality and integrity of the keys. We believe that our proposed solution will help cloud service providers and organizations to manage their cryptographic keys more efficiently and securely, reducing the risk of unauthorized access and data breaches. This research paper provides a method to effectively manage, store and share the secret key between the parties using the concept of Threshold Cryptography. © 2023 IEEE.

关键词： Access control

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