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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...

引用

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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HBTrackr: AI-Based Heart Attack Prediction through ECG Monitoring on Wearable Devices 1

HBTrackr: AI-Based Heart Attack Prediction through ECG Monit...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Micheal, A Ancy Gupta, Kriti Singh, Rishi Singh, Vikram Mohanty, Bipasha School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350365016

The topic proposes the existence of various mobile applications designed to predict and detect abnormal heartbeats, providing individuals with an accessible tool for monitoring their cardiac health. Advancement in AI supports heart attack prediction which can be deployed in various smart applications. In the paper, a novel AI model is implemented to read the Electrocardiogram (ECG) of the patient and understand the heart health of the person by wearing a smart watch. The proposed framework focuses on detection of sudden cardiac arrest and prediction of abnormal heartbeat using You Only Look Once(YOLOv8) in the wearable devices. The model is trained on ECG images dataset of cardiac patients using a matrix of features such as myocardial infarction, irregular heart rhythms, and healthy cardiac patterns. The model achieves high precision, recall, and F1 score, demonstrating its effectiveness in accurately classifying cardiac conditions. HBTrackr is an easy-to-use smartwatch app which is incorporated with an AI model that uses the pre-existing sensors in the watch. © 2024 IEEE.

关键词： Watches

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Dynamic-DETR: Dynamic Perception for RT -DETR 6

Dynamic-DETR: Dynamic Perception for RT -DETR

引用

6th International Conference on Robotics and computer Vision, ICRCV 2024

作者： Wei, Jianghao Shen, Wei Hu, Tao Liu, Qiang Yu, Jie Huang, Jiahuan School of Computer Science and Engineering Wuhan institute of Technology Wuhan China

ISBN: (纸本)9798331527426

Object detection is a crucial branch of computer vision. RT-DETR, a recent advancement building upon DETR, has showcased superior performance over YOLO in real-time object detection. However, RT-DETR may struggle with complex inputs due to its limited dynamic perception. To address this, we propose Dynamic-DETR, an enhanced object detection algorithm characterized by its dynamic and flexible nature. In the backbone stage, we introduce the ideas of adaptive dilation and deformability to enable the model to adapt to complex scale variations and geometries;in the neck stage, we introduce content-aware feature reassembly to further enhance the dynamic perception of the model. Experiments have shown that our model outperforms RT -DETR in the 2D supervised object detection task on the KITTI and SODA10M datasets. © 2024 IEEE.

关键词： Object recognition

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Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation

Exploring the Efficacy of Federated-Continual Learning Nodes...

引用

2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Jesher Joshua, M. Adhithya, R. Sree Dananjay, S. Revathi, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350363289

Web phishing poses a dynamic threat, requiring detection systems to quickly adapt to the latest tactics. Traditional approaches of accumulating data and periodically retraining models are outpaced. We propose a novel paradigm combining federated learning and continual learning, enabling distributed nodes to continually update models on streams of new phishing data, without accumulating data. These locally adapted models are then aggregated at a central server via federated learning. To enhance detection, we introduce a custom attention-based classifier model with residual connections, tailored for web phishing, leveraging attention mechanisms to capture intricate phishing patterns. We evaluate our hybrid learning paradigm across continual learning strategies (cumulative, replay, MIR, LwF) and model architectures through an empirical investigation. Our main contributions are: (1) a new hybrid federated-continual learning paradigm for robust web phishing detection, and (2) a novel attention + residual connections based model explicitly designed for this task, attaining 0.93 accuracy, 0.90 precision, 0.96 recall and 0.93 f1-score with the LwF strategy, outperforming traditional approaches in detecting emerging phishing threats while retaining past knowledge. © 2024 IEEE.

关键词： Phishing

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Skin Cancer Detection using Image Processing and Machine learning 3

Skin Cancer Detection using Image Processing and Machine lea...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Rajkumar, S. Jaiswal, Shivam Jenila Livingston, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372120

Skin cancer, with melanoma being the most perilous type, has a low survival rate. Detecting melanoma early can greatly enhance survival prospects. Skin cancer detection technology consists of the key components: image preprocessing (involving tasks like hair removal, noise reduction, and resizing), segmentation (used to isolate the region of interest), feature extraction from the segmented image, and image classification based on extracted features. In the proposed system section Convolution Neural Network (CNN) has been used as a base algorithm for training my model followed by training my model on ResNet and DenseNet and based on the evaluation results. Fine-tuning has been applied to adjust the learning rate dynamically. Different methods can be used for segmentation. Some commonly used segmentation algorithms are threshold in the histogram, etc., features extraction from the segmented image, and classification of the image from the features set extracted from the segmented image. © 2024 IEEE.

关键词： Diseases

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

Classification, Segmentation and Quantification of acute lym...

引用

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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Community Detection within the US House based on Policy Voting 9

Community Detection within the US House based on Policy Voti...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Kumar Singh, Sanskriti Sanjay Sinha, Apoorv Kumar Nair, Atul Nathezhtha, T. School of Computer Science And Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350365092

With an increase in the rise of divisive politics that are often contrarian to the core beliefs and manifesto of political parties, it is become imperative for political parties and candidates to find a solid middle ground where they both compromise. However, this compromise cannot be integrated at time of voting which is why it is very important to cluster defectors. Clustering analysis is instrumental in the process of understanding possible relationships between defectors and their original party. For instance, if a defector consistently votes against his own, they simply wouldn't be part of it but that is not the case. Defections usually happen due to a specific policy or more importantly, the domain of a political policy. As such, seeing where the defector concurs with the opposition is desired objective of the proposed study. The proposed model utilizes the Louvain Walk as aclustering methodology due to its focus on modularity measures to understand the vitality of a node to a particular subnetwork and cluster while also putting an emphasis on the cardinality of its relationship with those amongst its cluster. Due to existing target labels, it'll also be uncovered what policies and which states cause the most defections and how they interact with each other which can only be done bygraph analysis. © 2024 IEEE.

关键词： Defects

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