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Object Detection in Images Using machine learning

Object Detection in Images Using Machine Learning

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： G. Nagarajan Abhiram Piratla Karthikeya Golla Department of CSE Sathyabama institute of science and technology Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This study develops a scalable, effective, and user-friendly solution to tackle the problem of real-time object detection in photos. The suggested approach makes use of YOLOv5 (You Only Look Once version 5) for deep learning-based object detection and Flask for managing REST API queries. Users can submit base64-encoded photos to the system, which will analyze them using the YOLOv5 model to identify and categorize objects, label them with bounding boxes, and then return the base64-formatted images. High-speed performance is ensured by using PyTorch for model inference, while Non-Maximum Suppression (NMS) increases object localization accuracy by removing redundant detections. Because of its lightweight architecture and ease of integration with online applications, this system can be used for a variety of purposes, including surveillance and e-commerce. The study's output is a reliable, approachable object detection API that can effectively manage real-time image processing and deliver precise item recognition, showcasing the potential for implementing deep learning models in intuitive and scalable web-based settings.

关键词： YOLO Deep learning Accuracy Surveillance Medical services Streaming media Real-time systems Object recognition Reliability Streams

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machine learning-Based Crop Recommendation System for Mizoram 3

Machine Learning-Based Crop Recommendation System for Mizora...

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Kumar, V.D. Ambeth Kumar Khan, Ajoy Vanlalhruaia Saithantluanga Ramesh Prabhakaran, R. Zaitinkhuma Mizoram University Dept. of Computer Engineering Aizawl India

ISBN: (纸本)9798331523893

Agriculture is an important sector of Mizoram domicile as more than half of its population relies on Agriculture as principle source of income and sustenance. Some farmers rely on the knowledge acquire from their parents through explicit explanation, and by observing and modelling their practices. But most farmer often struggle to understand the method and type of crops to cultivate for better crop yield. Even experienced farmer believed that using more fertilizer result in better crop yield in spite of that it damages the soil properties. To resolve this challenge, this paper presents a Crop Recommendation System using machine learning, tailored for Mizoram, enhancing Agriculture practices towards sustainable development. The Crop Recommendation System analyzes historical data, soil properties, weather pattern and crop performance to recommend the best crop for a specific region and its condition. The aim is to provide the Crop Recommendation System with information about the soil and the condition of the region. The study utilizes various machine learning Algorithms such as Random Forest, Decision Trees, Support Vector machine and Logistic Regression to make optimal recommendation. The results indicate that Random Forest provides superior performance of 99% across all the evaluations metrics. Although many prevention measures need to be taken to avoid complications such as overfitting, etc. The overall results suggested that Random Forest achieved the best results as compared to all the other state of the art algorithms utilized with the same preprocessing steps. This approach enhances the crop and soil. After a long and often complicated process of farming method and selection of crop problem the Crop Recommendation System will aid Mizoram farmers to achieve better crops, yield and higher profit. © 2025 IEEE.

关键词： Agriculture Crop Recommendation (CRS) System Decision Trees machine learning Mizoram Random Forest Support Vector machines

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Intelligent Parking System to Prevent Unauthorized Parking of Vehicles Using IoT and R-CNN with YOLO

Intelligent Parking System to Prevent Unauthorized Parking o...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sushma Vispute Manasi Potdar Pranjal Pawara Shreya Patil Satvika Patil Dinesh Bhaskar Ahirrao Dept. of Computer Engineering Pimpri Chinchwad College Of Engineering Pune India KCES's College Of Engineering and Management Jalgaon India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In recent years., the proliferation of private vehicles has led to a significant challenge in finding adequate parking space., with unauthorized parking becoming a prevalent issue in many residential societies. To address this problem., we propose a novel solution that integrates machine learning techniques and motion detection technology to effectively manage parking spaces and curb unauthorized parking. Our approach utilizes advanced machine learning models such as RCNN for object detection and YOLOv8 for license plate detection., complemented by optical character recognition (OCR) for character recognition. Additionally., the ESP32-CAM webcam module is employed to capture real-time images., ensuring seamless integration with our system. This combination of technologies allows for the accurate detection of unauthorized parking instances in real time., providing efficient management of parking spaces within societies. Key features of the system include automated vehicle identification., real-time monitoring., and notification alerts., all of which contribute to a robust verification system for resident and guest vehicles. By leveraging these advanced technologies., our solution not only streamlines the parking process but also promotes a more organized and peaceful living environment. This innovative approach addresses a critical issue in urban settings., paving the way for more efficient and sustainable parking management practices.

关键词： YOLO Space vehicles Accuracy Webcams machine learning Real-time systems Internet of Things Character recognition Reliability License plate recognition

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Hand Mudras and Indian Classical Dances: Techniques, dataset and Future Direction

Hand Mudras and Indian Classical Dances: Techniques, Dataset...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Chayanika Sarmah Parismita Sarma Department of Information Technology Gauhati University Guwahati Assam India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Concept, purpose and feelings of a dance is expressed through performer’s gesture. Proper interpretation of these signals requires skills acquired by different methods, artificial intelligence or human experts. In recent times, Dance Mudra recognition is a very prominent and a multidisciplinary research topic. This paper discusses extensive research work carried out on various Indian classical dances by different researchers and automated recognition system developed by them. Author’s basic focus here is to study and discover an efficient intelligent method which will be capable of recognizing different hand mudras of Sattriya dance. A few efficient machine and deep learning methods are selected for working on our sattriya dance mudra dataset. The authors of this paper have used customized CNN model, ResNet50, ViT and YOLOV8 for training the data and were able to achieve very good accuracy. Till now no work is done using the above mentioned models for recognition of sattriya hand mudras.

关键词： Hands Deep learning Training Humanities Accuracy Computational modeling data models Intelligent systems Residual neural networks

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A Visionary Analysis on Ways and Techniques Used for Identification of Citrus Plant Disease

A Visionary Analysis on Ways and Techniques Used for Identif...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Arundhati Bora Parismita Sarma Dept. of Information Technology Gauhati University Guwahati India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

This review paper discusses the identification of different leaf diseases in plants with specific reference to the lemon group. In recent times, it has been seen that leaf diseases can be identified very accurately using different machine learning and deep learning methods from the digital images of the infected leaves. The accuracy varies depending on the tagged sample size, variation of the samples, and method used. In this paper, the authors have analyzed almost forty (40) research papers and prepared a number of tables for easy understanding of techniques used and their performance in terms of percentage of accuracy. Segmentation of leaves is performed in most techniques in the image pre-processing phase. Analysis is carried out on the given tables from different perspectives and a future way of working is formulated from this survey of research.

关键词： Deep learning Surveys Plant diseases Accuracy Reviews Computational modeling Transfer learning Neural networks data models Object recognition

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ST-CNN+Transformer: A Novel Approach for data Fusion and Urban Functional Zone recognition 25

ST-CNN+Transformer: A Novel Approach for Data Fusion and Urb...

引用

Proceedings of the 2025 3rd international conference on Communication Networks and machine learning

作者： Jiahui Yang Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798400713231

Accurately delineating and identifying different functional areas in cities (e.g., commercial, residential, industrial zones) is crucial for urban development and the creation of smart cities. With the rapid advancements in remote sensing technology and big data, auxiliary data, such as Points of Interest (POI), have become key resources for improving functional area recognition accuracy. However, the heterogeneity and uneven spatial distribution between remote sensing imagery and POI data present significant challenges for data fusion and functional area identification. Although traditional CNNs have shown great success in image classification, they face difficulties in handling multi-source data fusion. This paper proposes an innovative solution to address these challenges. First, we optimize the traditional CNN by introducing a spatiotemporal rule-based fusion mechanism (ST-CNN), incorporating cutting-edge deep learning techniques. Second, we employ Transformer technology to enhance the model's performance to capture complex spatial patterns for functional area recognition. The combined framework, ST-Former (ST-CNN + Transformer), improves accuracy by 3%-15% compared to other classic CNN models. Additionally, when data fused through ST-CNN is input into other methods, accuracy improves by 1%-5%, balancing reconstruction performance with network complexity.

关键词： CNN

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3rd Workshop on mining data for Financial Applications, MIDAS 2018 and 2nd international Workshop on Personal Analytics and Privacy, PAP 2018 held at 18th European conference on machine learning and Knowledge Discovery in databases, ECML PKDD 2018

3rd Workshop on Mining Data for Financial Applications, MIDA...

引用

3rd Workshop on mining data for Financial Applications, MIDAS 2018 and 2nd international Workshop on Personal Analytics and Privacy, PAP 2018 held at 18th European conference on machine learning and Knowledge Discovery in databases, ECML PKDD 2018

ISBN: (纸本)9783030134624

The proceedings contain 12 papers. The special focus in this conference is on mining data for Financial Applications. The topics include: Privacy risk for individual basket patterns;exploring students eating habits through individual profiling and clustering analysis;calibrating the mean-reversion parameter in the hull-white model using neural networks;deep factor model: Explaining deep learning decisions for forecasting stock returns with layer-wise relevance propagation;a comparison of neural network methods for accurate sentiment analysis of stock market tweets;a progressive resampling algorithm for finding very sparse investment portfolios;ICIE 1.0: A novel tool for interactive contextual interaction explanations;testing for self-excitation in financial events: A bayesian approach;a web crawling environment to support financial strategies and trend correlation: – extended abstract –;a differential privacy workflow for inference of parameters in the rasch model.

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Target recognition and Tracking Using You Only Look Once Version 5 with DeepSORT in Unmanned Aerial Vehicle Vision Systems

Target Recognition and Tracking Using You Only Look Once Ver...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Shuhan Yu Hainan International College Communication University of China Beijing China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Nowadays, the target recognition and tracking technology in Unmanned Aerial Vehicles (UAV) vision systems has gained considerable interest due to its promising implications for various applications. However, the existing Open Flow based Tracking (OFT) system faced challenges by factors such as varying lighting conditions and occlusions. Hence, this research proposes a You Only Look Once version 5 with Deep learning based Simple Online Real-Time Tracking (YOLOv5-DeepSORT) is proposed to handle the occlusions. The proposed YOLOv5-DeepSORT detect and track objects across frames for accurate target detection and tracking in UAV vision systems. Initially, the input data is collected from Vision-based Drone (VisDrone) dataset and then, the data augmentation is performed to expand the dataset variability. After that, YOLOv5 is employed for object detection by incorporating Transformer based Multi-Head Attention (T-MHA) as backbone to extract features. Then, GhostNet is used as feature fusion technique to minimize the computational overhead of the network. Finally, DeepSORT is introduced to track the objects by predicting object motion and associating object across frames. From the results, the proposed YOLOv5-DeepSORT achieved better results in terms of recognition accuracy (99.5%), recognition speed (33.7 vehicles/sec), and manual time (1.4s) when compared with existing UAV-Vision Based Moving Target Detection (UAV-MTD).

关键词： YOLO Deep learning Target tracking Accuracy Target recognition machine vision Autonomous aerial vehicles Feature extraction Transformers Real-time systems

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Cyberbullying Prediction Using K-Means Clustering with learning Methodologies for Emotion Detection in Tweets

Cyberbullying Prediction Using K-Means Clustering with Learn...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： P Privietha R Sasirekha L Melwin Akash K Punitha A Marshal Felix R Ramyabharathi Department of CSE Sathyabama Institute of Science and Technology chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The rise of social media has led to an increase in real-time data that can reveal potential cyber threats. This research explores the use of emotion detection in tweets to predict cyber attacks, employing K-Means Clustering in combination with machine learning methodologies. By analyzing emotional trends, such as fear, anger, and anxiety, this study establishes a correlation between social media sentiment and cyber attack occurrences. The results demonstrate the potential for early warning systems that harness emotional patterns for cybersecurity. Social media platforms like Twitter provide a wealth of real-time data that can reveal societal trends, including indicators of potential cyber threats. This study investigates the use of emotion detection in tweets as a predictive tool for cyber attack forecasting. By employing Natural Language Processing (NLP) techniques, emotional states such as fear, anger, and anxiety are extracted and analyzed. K-Means Clustering is utilized to group similar emotional patterns, enabling the identification of significant trends. These clusters, combined with historical cyberbullying data, are used as features in supervised learning models for cyber threat prediction. The results demonstrate the effectiveness of integrating unsupervised clustering and machine learning to correlate spikes in negative emotions with the likelihood of cyber incidents. This research highlights the potential for proactive cybersecurity strategies that leverage social media sentiment as an early warning system.

关键词： Support vector machines Emotion recognition Anxiety disorders Cyberbullying machine learning Alarm systems Market research Natural language processing Real-time systems Cyberattack

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Enhancing Real Estate Market Predictions: A machine learning Approach to House Valuation

Enhancing Real Estate Market Predictions: A Machine Learning...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Pradeep Jha Manju Mathur Abhay Purohit Apoorva Joshi Anantika Johari Suhani Mathur Department of CSE Global Institute of Technology Jaipur Rajasthan India Department of IT Global Institute of Technology Jaipur Rajasthan India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

machine learning (ML) has emerged as a versatile technology, transforming various domains such as speech recognition, medical diagnostics, and real estate analytics. This research addresses the critical challenge of accurately predicting house prices in dynamic and volatile real estate markets. Traditional valuation methods often fail to capture complex relationships between features, leading to suboptimal predictions. To mitigate these limitations, this study proposes a predictive model leveraging multiple ML algorithms, including Linear Regression, Decision Tree Regression, K-Means Clustering, and Random Forest Regression. Among these, Random Forest Regression demonstrated superior accuracy, achieving a root mean square error (RMSE) of 2.913. The findings indicate that integrating advanced ML techniques can streamline the home-buying process by enhancing price prediction accuracy, reducing dependency on intermediaries, and enabling informed decision-making for stakeholders. This study provides a robust framework for applying ML in real estate and highlights opportunities for further advancements in predictive modeling.

关键词： Accuracy Speech recognition Predictive models Prediction algorithms Stakeholders Medical diagnosis Root mean square Random forests Regression tree analysis Cost accounting

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