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Detecting Helmets Of The Bike Riders Using Deep Learning Algorithms 14

Detecting Helmets Of The Bike Riders Using Deep Learning Alg...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mia, Yousuf Rahman, Md. Solaimanur Basak, Animesh Hossain, Sheikh Mufrad Zulfiker, Md. Sabab Sumaia, Merina Akter Daffodil International University Dept. of Computer Science & Engineering Dhaka Bangladesh Model Institute of Science & Technology Dept. of Computer Science & Engineering Gazipur Bangladesh

ISBN: (纸本)9798350335095

We propose real-time bike helmet detection in our study. A lot of people ride bikes in our nation. Motorbikes are more popular than vehicles because they are cheaper to maintain, take up less parking space, and provide more mobility and adaptability in urban situations. Bike riding is entertaining yet risky. Bicyclist safety is the planned system's main purpose. Many drivers don't wear helmets even though they're mandated by law. In emerging countries, mortality has been growing steadily. A helmet detection technology that identifies drivers without helmets is needed to safeguard the public. We employ a real-time 3202 dataset for this approach. We gather wearing helmet 1911 and no helmet 1291 data and utilize algorithms like VGG16, Resnet50, MobileNet v.02, Inception V3, EfficientNet, and CNN. The EfficientNet achieved 98% accuracy. Each person's comparison statement techniques are in the implementation section. To construct the optimum model for the conditions, this inquiry uses model validation approaches. © 2023 IEEE.

关键词： Deep learning

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Personal Finance Management and Prediction using ML Algorithms 8

Personal Finance Management and Prediction using ML Algorith...

引用

8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kaushik, Sujay G. Kashyap, Sumukha S. Surabhi, B.V. Chaitra, M. Reddy, Santhosh Anitha, N. Bnm Institute of Technology Affiliated to Vtu Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Effective personal finance management is essential for achieving financial stability and long-term financial health. This paper presents a comprehensive approach to summarizing, analyzing, and show monthly expenses using machine learning models. By collecting user inputs such as age, place of stay, number of dependents, monthly expenses on transport, education, subscriptions, savings, medical expenses, and more, the system uses linear regression, decision trees and random forest algorithms to predict financial values. The system provides detailed information of expenses, savings, and overall financial health, along with personal health analysis based on dietary habits. Users have access to a wide range of financial and lifestyle information, allowing the system to provide personalized recommendations to increase financial security and achieve financial goals. The model also evaluates the impact of recurring expenses and notable investments, helping users understand their spending patterns and make informed financial decisions. The results demonstrate the system's capability to deliver accurate predictions and actionable advice, enable users to better manage their finances. This approach has led to significant advances in personal financial management by integrating machine learning techniques to provide insight into people's financial health. © 2024 IEEE.

关键词： Adversarial machine learning

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Fine-tuning Llama-2-7b Model for Conversation Summarization 15

Fine-tuning Llama-2-7b Model for Conversation Summarization

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nivethitha, V. Verma, Nirvik Bokadia, Ronit Jaju, Ronith Vellore Institute of Technology Chennai School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350370249

In this research paper, we explore the optimization for conversation summarization of the Llama 2.7 b model by quantization-aware fine-tuning, specifically exploiting QLORA quantization techniques. In natural language processing (NLP), large language models (LLMs) have become powerful tools for various text-processing tasks including dialogue summarization in a rapidly evolving landscape. Still, the computational needs of these model's present obstacles to their practical implementation in conversational interfaces and live applications. Quantization observance fine-tuning is an optimistic way of compressing model parameters while still enhancing performance. This analysis demonstrates through rigorous experimentation that the QLORA quantization is effective on fine tuning the Llama 2.7b model for conversation summarization. Fine-tuning the model on a well-rounded dataset with conversational data derived from different sources such as social media, chat logs, and customer interactions makes it more adaptable to any sort of dialog that takes place. The incorporation of QLORA quantization's also improves the attention mechanism of the model enabling it to obtain relevant information such as generating concise summaries. The latest version of Llama is 2.7. It is called Llama 2.7b and provides several practical advantages for summarizing dialogues such as improved computational efficiency, better user experience in conversational interfaces, and capturing key insights from long conversations. Its outputs are in line with the current research on LLM-based conversation summarization and exhibit the viability of optimization techniques like quantization-aware fine-tuning to maximize the performance of large language models for particular natural language processing tasks (NLP). © 2024 IEEE.

关键词： llama 2 LLM Natural Language Processing QLORA Quantization

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Age Based Hybrid Recommendation System using Machine Learning 3

Age Based Hybrid Recommendation System using Machine Learnin...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Karthikeya, Andhoju Kumar, Alle Aniesh Manicharan, Chennuri Hariharan, Shanmugasundaram Kukreja, Vinay Prasad, Andraju Bhanu Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350303360

The recommendation system is one of the bigger developments brought about by machine learning in the machine-human interactions. In practically every area, including entertainment, e-commerce, farming, health, etc., recommendation systems are the primary players in delivering greater personalization. When it comes to content-based recommendation systems, there are many social platforms which uses recommendation system for providing improved personalization to their users and to access these social platforms the user need to have an account in that particular social platform. These platforms have different categories of content where not everyone is eligible to a particular content category. There are scenarios where different users may use the same account to access an entertainment application, or if a user inadvertently watched some inappropriate content, the recommendation system will make inappropriate recommendations, which can have an impact on the user's mental health-children. Therefore, after conducting research on the problem statement and reviewing various existing research works, machine learning model is proposed that offers recommendations to the user after determining the user's age and gender in order to deliver appropriate recommendations to the users. © 2023 IEEE.

关键词： Recommender systems

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An Improved Intrusion Detection system for securing academic information on the cloud using a blockchain-based security framework 2

An Improved Intrusion Detection system for securing academic...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dubey, Krishna Bihari Goyal, Mukta Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350371055

The concept of cloud computing has hastily grown in reputation, allowing businesses to shop and get access to facts from remote servers. Higher educational establishments have additionally adopted cloud services for storing and dealing with academic statistics. But this additionally poses a capability chance to the security and privacy of touchy facts. To deal with those challenges, we advise an advanced intrusion detection system (IDS) to secure the cloud's academic data. This ID uses a blockchain-based total safety framework to ensure the safety and integrity of stored statistics. The proposed IDS utilizes community visitors' monitoring and analysis to stumble upon anomalies and potential protection breaches. It also employs machine-learning techniques like anomaly detection algorithms to identify suspicious activities deviating from everyday behaviour styles. In the event of a detected intrusion, the device triggers an alert to the responsible parties and makes essential movements to mitigate the assault. The combination of blockchain technology offers an additional layer of security by developing an immutable and decentralized ledger to report all data transactions. This guarantees the integrity and authenticity of the academic statistics stored on the cloud. In case of any modifications or changes to the records, the blockchain will record them, creating a transparent audit path for accountability. Furthermore, the proposed IDS also consists of a belief management gadget that evaluates cloud carrier providers' popularity and protection posture. © 2024 IEEE.

关键词： Authenticity Cloud Community Decentralized Detected

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Flood Survivor Detection through Image Fusion and Yolo Model 3

Flood Survivor Detection through Image Fusion and Yolo Model

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Vinothini, R. School of Computer Science and Engineering Vellore Institute of Technology Vellore Tamil Nadu 632014 India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361186

Natural disasters like flood happen when water overtakes normally dry places. During rescue missions aimed at saving lives, rescue teams responding to floods encounter numerous obstacles. Locating and discovering survivors is a vital component of rescue operations. For quick flood survivor recognition, the suggested approach combines image fusion with the YOLO (You Only Look Once) model, offering a novel method. This model utilizes image fusion by merging data from various sources, including RGB and Near Infrared Red (NIR) images captured by Unmanned Aerial Vehicles (UAVs). In order to efficiently identify flood survivors and aid rescue crews in their effort to preserve lives, the resulting fused image is processed using the YOLO model. © 2024 IEEE.

关键词： Image fusion

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Crime Prediction by Comparing Machine Learning and Deep Learning Algorithms 2

Crime Prediction by Comparing Machine Learning and Deep Lear...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Malik, Kush Pandey, Manas Khan, Asma Srivastav, Manvesh Gera, Anju Chauhan, Sansar Singh G L Bajaj Institute of Technology and Management Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Preventing crime is important for justice and safety in cities. Using computers to predict crime trends can help make cities safer. Reliable real-time crime prediction is necessary for public safety, but there are still difficulties facing science. Crime rates are influenced by numerous intricate factors. Crime is low in comparison to many predictable events. For computer systems, determining the criminal activity rate, kinds, and hotspots based on historical patterns is both a challenge and an opportunity. The accuracy of machine learning using SVM stacking, KNN, Naïve Bayes, Random Forest, and deep learning using LSTM are compared in this study. This paper presents a comprehensive review of the literature on deep learning and machine learning approaches to crime prediction. It's an important tool for scholars studying the subject because it provides information that law enforcement organizations can use to improve how they prevent and deal with criminal activity. Moreover, the system outlined in the paper offers predictions about potential future crimes, allowing for proactive measures to be taken to prevent them. © 2024 IEEE.

关键词： Crime prediction deep learning KNN LSTM machine learning Naïve Bayes Random Forest SVM stacking

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DevConnect: Knowledge Sharing Social Platform

DevConnect: Knowledge Sharing Social Platform

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Jindal, Mayank Tomer, Rahul Dhingra, Deepti Gandhi, Anju Mahajan, Aakanksha Panipat Institute Of Engineering And Technology Department Of Computer Science And Technology Panipat India Panipat India

ISBN: (纸本)9798350306446

"DevConnect"aims to create an interactive platform tailored for developers to collaborate, seek assistance, and discover new projects seamlessly. Through its innovative features, developers can engage in meaningful collaborations, exchange messages for help queries, and explore fresh project ideas effortlessly. At the heart of the platform lies its ability to facilitate collaboration among developers, a vibrant community where knowledge sharing thrives. Users can reach out to fellow developers for assistance, feedback, or guidance, enriching their development journey with valuable insights and support. Central to the platform{\prime}s functionality is its robust search feature, enabling developers to explore a diverse range of projects based on their interests, skills, and preferences. Whether seeking inspiration for a new endeavour or searching for specific project types, developers can easily navigate the platform to discover exciting opportunities. Furthermore, developers have the option to follow their favourite peers, allowing them to stay updated on their latest projects, contributions, and insights. This feature not only cultivates a sense of camaraderie within the community but also empowers developers to build meaningful connections and expand their networks. The Social Media Showcase project is designed with user-friendliness in mind, ensuring that developers can effortlessly navigate the platform and access its wealth of resources. With its intuitive interface and seamless functionality, developers can focus their energy on what matters most: innovation and collaboration. By providing a dynamic and interactive space for developers to connect, share, and discover, the Social Media Showcase project seeks to enrich the development community and inspire creativity in the ever-evolving landscape of technology. © 2024 IEEE.

关键词： Social networking (online)

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Layoffs Analysis and Prediction Using Machine Learning Algorithms 6th

Layoffs Analysis and Prediction Using Machine Learning Algor...

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Prakash, P. Sakthivel, V. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9789819971367

This research aims to develop a model that can predict layoffs within a company using a combination of financial performance, industry trends, and company demographics as input variables. A machine learning algorithm was trained and tested using a dataset of historical data to identify patterns and correlations between these factors and layoffs. The model was then evaluated using a separate dataset to determine its accuracy in predicting future layoffs. The results showed that the model performed well, with high degree of accuracy in predicting layoffs. The research provides valuable insights for companies to better prepare for potential workforce reductions and for employees to anticipate potential job loss. The ability to predict layoffs can also help companies make better decisions on how to manage their workforce and minimize the impact on their employees. This research provides a valuable tool for companies to better plan for future workforce reductions, and for employees to be more aware of potential job loss. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Optimal Trained Ensemble of Classifiers for Brain Age Prediction Using MRI

Optimal Trained Ensemble of Classifiers for Brain Age Predic...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Vishnupriya, G.S. Rajakumari, Brintha S. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350382693

Prediction of brain age is necessary for a better understanding of the development of the human brain and ageing. Recently MRI data have been used for the estimation of brain age. However, variance in the development of brain age between the different as well as similar subjects gives variations in the developments at different ages. Therefore, advanced models are needed to predict brain age more accurately. Hence, this paper intends to propose an optimally trained ensemble classifier for brain age prediction by using MRI data. Initially, the MRI image is preprocessed by using the Bilateral Filtering process to eliminate the unwanted noise. Subsequently, the preprocessed images are segmented by the DBSCAN model and followed by feature extraction. Features such as LGBPHS, statistical features and pixel features are extracted from the segmented image. Finally, the prediction process is done via an ensemble classification model, which is a combination of RNN, Deepmaxout and GRU. Moreover, the weights of all the classifiers are optimally tuned by a proposed Self Improved-Bald Eagle Search Optimization (SI-BESO) to enhance its performance. The performance of the suggested ensemble model is evaluated over the conventional algorithms as well as classifiers in terms of performance measures like MAPE, MSE, MSLE, and MAE. © 2024 IEEE.

关键词： Image segmentation

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