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A Real-Time Video Detection Algorithm for UAV Based on YOLOv8 7

A Real-Time Video Detection Algorithm for UAV Based on YOLOv...

引用

7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Nie, Xin Wu, Qili School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9798350350760

Aiming at the common problem of small target detection in UAV real-time video surveillance, this paper proposes a UAV video detection algorithm based on improved YOLOv8 framework. By embedding a feature enhancement module specifically for small targets in the original YOLOv8 architecture, the algorithm effectively improves the recognition degree of the network for small targets. In order to avoid the excessive expansion of network parameters, the header network is simplified, the convolutional layer is removed and the number of channels is reduced to maintain the computational efficiency of the algorithm. Then this paper introduces the Channel Space Attention Mechanism module (CSAM), which makes the network pay more attention to the small target-dense region on the feature map by optimizing the weight distribution, thus enhancing the pertinence of target detection. In addition, a location-based label assignment strategy (LB-SimOTA) is proposed, which assigns different weights according to the intersection ratio (IoU) between the prediction frame and the real frame to optimize the overall quality of the network prediction frame. Experimental results on the VisDrone2019 dataset containing many small targets show that compared with the benchmark model YOLOv8-S, the proposed algorithm improves the accuracy of vehicle and pedestrian detection tasks by 8.44% and 8.12%, respectively, and achieves a frame rate of up to 90 FPS while maintaining the detection speed. ©2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Intrusion Detection System Using Hybrid Model of Denoising Autoencoder and Ladder Variational Autoencoder 10

Intrusion Detection System Using Hybrid Model of Denoising A...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Navyakala, SriramVenkata Rathinam, Rajesh Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

In the realm of cyber-security, the identification and mitigation of network intrusions remain critical challenges. The deep learning based approach for intrusion detection was introduced on reconstructing network traffic dataset using 3 autoencoder architectures and classification using Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The proposed technique is assessed using 10% of the CIC-IDS-2017 dataset. Three autoencoder architectures (GDAE, LVAE, and Gaussian Noise-injected LVAE) are used to remove noise and extract relevant features. This technique successfully identifies network intrusions, with an overall performance of 99% across all assessment measures. © 2024 IEEE.

关键词： Intrusion detection

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Detecting Depression in Social Media using Machine Learning Algorithms

Detecting Depression in Social Media using Machine Learning ...

引用

2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Goyal, Shivansh Sriram, Ajay Abirami, S. Nathezhtha, T. Vellore Institute of Technology School of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331541170

Social media platforms such as Facebook, Twitter, and Instagram drastically change our world. After the Covid-19 outbreak, people become more inclined to discuss their sentiments and emotions on social media. Every year, over 300 million individuals worldwide suffer from depression, a prevalent mental condition that can lead to suicide. Early detection of depression on social media can help save lives. The proposed work aims to identify depressive qualities in tweets. The main challenge of this task is performing feature engineering and modeling to classify tweets as depressed or not. Additionally, estimating both depressive and non-depressive traits in a tweet is essential for fully understanding its content, which is crucial for determining how best to assist the user. We propose a probabilistic machine learning (ML) approach that provides not only a binary classification but also a probability estimate for the classification. This approach allows us to capture the uncertainty and varying degrees of depressive traits in tweets, leading to more nuanced insights. The proposed model is tested using datasets generated from mining depressive tweets and an equal number of random tweets, totaling 39,111 tweets. By improving data collection with precise keywords and enhancing feature engineering through proper tweet cleaning, the proposed model's accuracy increases by 3%. The probabilistic approach further enhances the model's ability to assess the likelihood of depressive characteristics. © 2024 IEEE.

关键词： Tweets

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Classification, Segmentation and Quantification of acute lymphoblastic leukemia cells in the bone marrow

Classification, Segmentation and Quantification of acute lym...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Jeffine, Theou Kiruthika, S. Joshan Athanesious, J. School of Computer Science and Engineering Vellore Institute of technology Chennai India

ISBN: (纸本)9798331510299

Cancer is a type of disease in which abnormal cells flourish and penetrate adjacent tissues, then spread to other parts of the body via the blood and lymph system. This abnormality results in DNA alterations in the bone marrow, which give rise to malignant cells in haematologic malignancies like leukaemia. This results in the abnormal production of white blood cells, where cancerous cells proliferate more quickly than healthy cells, compromising the ability of healthy cells to carry out their regular tasks, including fighting infections, distributing oxygen, and clotting blood. The primary objective was to create an efficient model that could classify the given microscopic image of bone marrow containing cancerous cells or not. If the image is classified as cancerous then the model will segment the cancer cells over the normal cells, in addition the model will quantify the presence of malignant cells to measure the spread in the blood. Segmentation and quantification along with the classification will provide better explainability to the model, thus to achieve this both the image processing techniques and the deep learning model are used to develop the Classification, Segmentation and Quantification (CSQ) of acute lymphoblastic leukemia cells in the bone marrow. The model is evaluated using the metrics like accuracy, precision, recall, specificity and F1score. Experimental results showed that the model performs well when compared to the existing methods. © 2024 IEEE.

关键词： Lung cancer

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Reverse engineering of Android Malware Classification Using Semi-Supervised Learning 10

Reverse Engineering of Android Malware Classification Using ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sujaykumar Reddy, M. Thota, Phanitha Sri Vellore Institute of Technology School of Computer Science Engineering Vellore India

ISBN: (纸本)9798350384369

The escalating prevalence of Android malware poses a substantial threat to online security, necessitating innovative approaches to combat this growing menace. Identifying novel and intricate malware variants remains a formidable challenge in the realm of software security. Existing malware detection methods primarily relying on static features often prove inadequate in countering sophisticated modern malware. While dynamic detection methods hold promise, they frequently fail to leverage the full spectrum of malware characteristics. This paper proposes a novel malware classification framework utilizing semi-supervised learning methods utilizing both unsupervised and supervised learning algorithms. We use the CCCS-CIC-AndMal-2020 dataset encompassing 13 prominent malware categories and 191 eminent malware families. Our results suggested that PCA+XGBoost accomplished by reducing the original dimensionality from 142 features to a more compact set of 48 features. The utilization of PCA in conjunction with the XGBoost model not only enhances the computational efficiency but also preserves the essential information required for classification. © 2024 IEEE.

关键词： Self-supervised learning

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Unveiling the Multidimensional Risk Landscape of Human Trafficking in India: A K-Means Approach 1

Unveiling the Multidimensional Risk Landscape of Human Traff...

引用

1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Rajan, Nitya Dasararaju, Shalini School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350377002

Human trafficking, a modern form a slavery, causes extreme pain and trauma by depriving victims of their basic right and dignity. Within South Asia, India emerges as a significant focal point of human trafficking activities. Our research provides a multidimensional risk analysis on a state level by analyzing Human Trafficking Data using 5 categories: Total Number of Cases, Cases per Lakh Population, Child Trafficking, Trafficking for Forced Labor, and Sex Trafficking. Using the K-Means algorithm, we classify states into 3 risk levels: High, Medium and Low Risk. Our research shows that risk distribution varies significantly based on the type of Human Trafficking, showing different prevalence in different states and calls for a more nuanced analysis and intervention to combat Human Trafficking. Furthermore, it highlights the importance of analyzing the data using population normalized metrics, showing smaller states can have high prevalence of trafficking cases with respect to their population but lower numbers overall. Finally, we provide a ranked list of the most vulnerable states to Human Trafficking based on a multidimensional analysis over all categories, the top 5 states being Orissa, Delhi, Telangana, Rajasthan and Bihar. © 2024 IEEE.

关键词： K-means clustering

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Low-Latency State Management for Real-Time Tasks in Edge Serverless 22

Low-Latency State Management for Real-Time Tasks in Edge Ser...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wen, Yanyan Xu, Guangping Wang, Jianshe Hao, Wei Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798331509712

Stateful serverless systems commonly adopt an architectural paradigm characterized by compute and storage separation within cloud data centers. Nevertheless, guaranteeing prompt response for real-time tasks at the edge becomes challenging due to network overheads. This paper introduces a Low-Latency state management framework for real-time tasks in edge serverless systems called LoLa, which adaptively places states proximate to functions, thereby mitigating delays in accessing states within edge serverless systems. Our approach aims at mitigating network latency and optimizing resource utilization by co-locating functions and states, thereby enhancing the system's overall efficiency. We introduce an adaptive strategy to coordinate the migration of states. It dynamically adjusts the positions of states based on historical data and real-time feedback. Additionally, we designed an in-memory state storage mechanism to facilitate low-latency access and implement a lightweight and fine-grained state management to ensure stored state consistency. Evaluation results showcase the efficacy of LoLa in reducing state read and write latency within edge serverless systems. Specifically, the average response latency is observed to decrease by 65.2% and 38.1% in the best and worst-case scenarios, respectively. © 2024 IEEE.

关键词： Edge computing Stateful Serverless

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PSR-Tree: A Novel Method for Personalized Trajectory Data Protection 22

PSR-Tree: A Novel Method for Personalized Trajectory Data Pr...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Chundong Song, Yixuan Zhao, Yongxin Tian, Yuhan Fang, Shunyao Tianjin University of Technology School of Computer Science and Engineering Tianjin China

ISBN: (纸本)9798331509712

Privacy concerns related to location-based services are becoming increasingly critical. To address the limitations of previous research, which has often neglected the temporal attributes of trajectories and relied solely on average noise addition methods, a trajectory data publishing scheme is proposed based on an R-tree storage structure. Initially, trajectory data is dimensionalized using a spatiotemporal optimal trajectory segmentation algorithm, and the preprocessed key trajectory data is organized into a PSR-Tree. Subsequently, an incremental privacy budget allocation strategy is applied to add noise to the trajectory data. Finally, a Hidden Markov Model is employed to constrain noise addition at the PSR-Tree nodes, fulfilling the requirements for trajectory data publishing. Experimental comparisons with existing methods demonstrate that this approach effectively addresses the issue of insufficient data usability and exhibits robust scalability. © 2024 IEEE.

关键词： Differential privacy

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Crime Mapping and Predictive Analysis of Crimes in Maryland, USA

Crime Mapping and Predictive Analysis of Crimes in Maryland,...

引用

2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Thomas, Beryl Abey Raja, Sivakami School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331540685

The issue of crime has long existed, both nationally and internationally. Several research have looked at previous crimes and provided appropriate preventive measures. However, even in recent years, there has been a high prevalence of crime. Therefore, the primary objective of this study is to analyze the crimes that took place in Maryland, USA, between 2016 and 2020. After pre-processing and cleaning the dataset, numerous analysis and visualization findings were drawn. Improving public safety and crime prevention in the state of Maryland requires first developing a predictive analytic and crime mapping technology. By examining past trends and data, it was possible to ascertain the days of the week, time of the day, and months of the year that crimes were most likely to occur. In addition, the location of these occurrences and the most frequent form of crime each year could be inferred. The three primary machine learning algorithms that were employed to predict the kind of crime that would occur given other factors were K Neighbors Classifier, Random Forest Classifier, and Logistic Regression. The accuracy was moderate to low. The Random Forest Classifier, Logistic Regression, and K Neighbors Classifier displayed an accuracy of 52.55%, 43.86% and 38.39%. Nevertheless, by displaying past crime data trends, law enforcement officials can identify crime hotspots, identify emerging trends, and allocate resources efficiently. This would enable law enforcements to anticipate and prevent crimes before they occur, allowing for proactive interventions. © 2024 IEEE.

关键词： Crime

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A Smart Agricultural Assistant for Crop Recommendation using Machine Learning 15

A Smart Agricultural Assistant for Crop Recommendation using...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, D. Kiranteja, P. Shridharan, V.J. Venkhat Desigan, P.K. Raghav, N.G. Pranav, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331300579

This paper presents a Smart Agriculture Assistant that uses data analytics and machine learning to assist farmers in making informed decisions about various farming practices. The assistant offers a holistic approach to enhance agricultural decision-making process by recommending crops based on soil attributes, climate, and local weather conditions, as well as providing precise fertilizer and pesticide guidance tailored to Indian contexts. Data is collected from various sources, such as weather forecasts, groundwater data, soil maps, and crop prices. Machine learning algorithms are trained on that data to generate optimal recommendations for farmers. The study demonstrates the effectiveness of smart agricultural techniques over conventional methods in improving crop selection accuracy and optimizing fertilizer application, thereby enhancing agricultural productivity. Furthermore, the integration of image analysis technology using computer vision enables the system to assess crop quality parameters, aiding in the detection of grade, size, colour, and disease presence. It also provides insights of estimated real-time crop prices by taking into account the fickle market dynamics. This offers valuable information to the farmers for increasing profit margins. The assistant is implemented as a web application that can be accessed by farmers from anywhere. This research provides a framework for the integration of data-driven technology to solve the unique issues encountered by Indian farmers. The Smart Agriculture Assistant presented here represents the power of data science and deep learning for a more prosperous and sustainable future for Indian agriculture. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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