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Smart Patrolling System Using machine learning 3

Smart Patrolling System Using Machine Learning

引用

3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Megala, P. Prabhu, R. Devi, V. Yamini, S. Sri Eshwar College of Engineering Department of Computer and Communication Engineering Coimbatore641202 India Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi642003 India Hindustan College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Coimbatore641032 India Karpagam Institute of Technology Department of Computer Science and Engineering Coimbatore641021 India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391565

The implementation of Smart Patrolling System using IoT is to provide a safety to the people those who are present in the area by the help of fixing a Sound Sensor, Ultrasonic sensor, Raspberry pi, Node MCU, Night vision camera, GPS, GSM in street lights. The Smart Patrolling System using IoT continuously monitor the place and it gives the signal to the people those who are living in the place first then the control room. The proposed system can be used in Residential areas for giving better safety to the people. The proposed system gives a very most importance to Women and children safety. By the use of our proposed system several crime attacks can be found immediately and it can be avoided from crucial situations. © 2024 IEEE.

关键词： Sensor nodes

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Vehicle Type Detection and Classification Using Improved Relief Feature Selection Algorithm and Bidirectional Gated Recurrent Unit 2

Vehicle Type Detection and Classification Using Improved Rel...

引用

2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Monica, K.M. Al-Farouni, Mohammed Madhura, G.K. Ghamya, K. Geethalakshmi, M. Vellore Institute Of Technology School Of Computer Science And Engineering Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Artificial Intelligence And Machine Learning Bengaluru India Tirupati India Kongunadu College Of Engineering And Technology Department Of Electronics And Communication Engineering Trichy India

ISBN: (纸本)9798350381665

Nowadays, Traffic density estimation can be utilized to control traffic light signals for managing the traffic effectively. Vehicle Type Classification (VTC) is used in this traffic density. Because of the factors like minimal interclass variance and maximum intraclass variation, the VTC is challenging which is handled by proposed Improved Relief Feature Selection Algorithm and Bi-directional Gated Recurrent Unit (IRFSA-Bi-GRU) method. Bi-directional Gated Recurrent Unit (Bi-GRU) model is proposed for enhancing Vehicle Type Classification (VTC) within Intelligent Transport Systems. Multiview In Out Traffic Complexity Database (MIOTCD) is used for data collection then a camera response model is used for enhancing visual data quality. Kalman filtering and Gaussian mixture model techniques are utilized for vehicle recognition and tracking. Local Binary Pattern (LBP), Steerable Pyramid Transform (SPT), and Dual Tree Complex Wavelet Transform (DTCWT) are used for feature extraction and Improved Relief Feature Selection Algorithm (IRFSA) is used for feature selection. Finally, the Bi-directional Gated Recurrent Unit (Bi-GRU) model is used for classifying 11 vehicle categories on MIO-TCD. The obtained results exposed that the IRFSA-BiGRU method outperformed the existing methods by achieving an accuracy of 99.02%, 98.34% F-score, 98.78% MCC,99.93% specificity, 99.32% NPV, 98.75% precision, 98.14% recall on the MIO-TCD database. © 2024 IEEE.

关键词： Classification (of information)

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Inter-Vehicular Communication Using Split Ring Resonator 2

Inter-Vehicular Communication Using Split Ring Resonator

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Ashok, S. Palanivel, S. Prasanth, P. Prianka, R.R. Prabhu, V. Peroumal, Vijayakumar Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Electronics and Communication Engineering Chennai India New Horizon College of Engineering Artificial Intelligence and Machine Learning Bengaluru India Vellore Institute of Technology School of Electronics Engineering Chennai India

ISBN: (纸本)9798350306415

For any wireless communication system, having an antenna is essential. However, it can be challenging to evaluate them, especially when developing new technology for intelligent transportation that requires communication between vehicles, with traffic efficiency and safety being crucial. To assess antenna performance, a novel technique that considers the multipath environment can be used in the early product development stages. By analyzing the radiation patterns of Split Ring Resonators (SRR) antennas mounted in various positions on the vehicle in a V2X environment, voltage readings can be obtained at the receiving antenna port to generate received power cumulative distribution functions (CDFs). Using these parameters, the antenna radiation patterns are assessed through a multipath simulation program, which presents the results as received power CDFs. © 2024 IEEE.

关键词： Antenna Split ring resonator V2X Vehicular Communication

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Real Time Object Detection and Tracking Using SSD Mobilenetv2 on Jetbot GPU

Real Time Object Detection and Tracking Using SSD Mobilenetv...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Ramesh G Jeswin Y Divith R Rao B.R Suhaag Daksh Uppoor Kiran Raj K M Dept. of Artificial Intelligence and Machine Learning Alva's Institute of Engineering and Technology Mangalore India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

The results of the experiments show that dataset size has a substantial impact on the accuracy of deep learning applications. For example, the Single Shot Detector (SSD) MobileNetV2 architecture, when trained on a dataset of 45,000 samples, achieved an impressive 97.8 % accuracy with an inference latency of 5.545 milliseconds (ms) on the Jetson Nano platform. In line with these discoveries, my research intends to combine the SSD MobileNetV2 model with Jetbot, a mobile robotics platform powered by NVIDIA Jetson Nano, to enable effective object tracking and detection. By using a pipeline that includes data collection, model training, inference, and motor control, the system optimizes performance by using pre-trained weights that may have been modified for certain datasets. Using tracking algorithms for constant movement monitoring, the system quickly recognizes and categorizes objects by utilizing real-time inference on the Jetson Nano. The system's practical usefulness in many circumstances, such as navigation, is shown via experimental validation, which also highlights the system's potential for enhancing autonomous systems and robotics.

关键词： Training Motor drives Accuracy Navigation Pipelines Object detection Very large scale integration Real-time systems Object tracking Robots

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Development of Deep learning Model for Detection of Abdominal Diseases using Radiological Images

Development of Deep Learning Model for Detection of Abdomina...

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Vidhi Wankhade Soyal Nimbalkar Kajal Dakhare Rohan Jangamwar Meher Langote Prateek Verma Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

As the abdominal and chest injuries are high morbidity and mortality around the globe, these pose an acute challenge in emergency care. Current data from recently released sources indicate that more than half of the patients visiting ER and also being admitted into hospital account for trauma; and the burden of both abdominal and chest injuries is quite high. Imaging plays an important role in diagnosis and treatment planning in the management of abdominal and chest injuries. The most widely practiced modality is radiograph imaging simply because it produces quality and easy results. Improving Automated pathology object detection of things in radiographs with deep learning algorithms promises much for the precision of diagnosis and optimization in health care. This paper discusses and provides a very detailed review of optimized CNNs, primarily ResNet18, ResNet34, along with a custom-built neural network for the identification of disease from X-ray pictures. Such models are trained and validated to reach excellent sensitivity and specificity on a wide variety of diseases by using the annotated RSNA 2023 dataset by professionals in radiology. It extended the scope of earlier research in this field by looking at other imaging conditions and diseases as well. This puts its contribution into the larger, ongoing discussion of AI in radiology. The results of this study help to contribute to the thesis that the promise of deep learning in radiological diagnosis is substantial, and it could be used to better enhance patient outcomes in the practice of emergency radiology.

关键词： Deep learning Pathology Ethics Neural networks Imaging Radiology artificial intelligence Injuries X-ray imaging Diseases

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Forecasting of Photovoltaic Power with ARO based AI approach 2

Forecasting of Photovoltaic Power with ARO based AI approach

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Nallur, Mounika Nalini, B.M. Khan, Zabiha Nayana, S. Achyutha, Prasad N. Manjula, G. Murray State University Computer Information Systems United States East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Bgs College of Engineering and Technology Department of Cse Bangalore India

ISBN: (纸本)9798350382952

Renewable power from sunlight can power the future smart grid with massive amounts of electricity. Systems struggle with solar energy's unpredictability and intermittent nature. Unpredictability of solar electricity hinders smart grid optimization and planning. Photovoltaic (PV) power generation must be accurately estimated to reduce power interruptions. PV power must be accurately predicted to avoid grid disturbances from PV facilities. Thus, we describe a transfer learning and AlexNet-based CNN architecture for short-term power forecasting. Past power, solar radiation, wind speed, and temperature readings determine the input. AlexNet's hyper-parameters are optimized using the artificial rabbit method. By adding selective opposition to ARO, local solution tracking efficiency is improved. CNN input features are created from all input parameters as 2D feature maps. After analyzing real PV data from Limberg, Belgium, the math shows that PV systems work. © 2024 IEEE.

关键词： Convolutional neural networks

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Optimizing Semantic Segmentation for Enhanced Football Analytics: A Pixel-level Approach 2

Optimizing Semantic Segmentation for Enhanced Football Analy...

引用

2nd International Conference on machine learning and Data engineering, ICMLDE 2023

作者： Malakreddy, Bharathi Venkataraman, Sadanand Khan, Mohammed Sinan Nidhi Padmanabhuni, Srinivas Natarajan, Santhi Research & Consultancy Cell BMS Institute of Technology & Management Bengaluru India Department of Computer Science and Engineering Research Shiv Nadar University Chennai India Department of Artificial Intelligence & Machine Learning BMS Institute of Technology & Management Bengaluru India *** Bengaluru India

Semantic Segmentation, a pivotal technique in image analysis, is adeptly leveraged in this research to bolster sports analytics, with a concentrated focus on football. A comprehensive pipeline is unveiled for an in-depth analysis of a select portion of the IAUFD 100k dataset, encompassing 2030 manually annotated football images. The methodology entails a thorough evaluation and comparison of diverse semantic segmentation models, supplemented by the integration of advanced pre-processing strategies and optimal training techniques. Such a holistic approach culminates in a marked enhancement in model performance, as evidenced by a significant uptick in the mean Intersection over Union (mIoU). This research offers granular, object-oriented insights that substantially augment player tracking, action recognition, and event detection in football. The conclusive remarks of the study highlight prospective avenues for further research, emphasizing the potential incorporation of Explainable AI and advanced Metamorphic and Security Testing to fortify sports analytics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sports

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PFCM Based Segmentation and TFA Based DCNN Model for Skin Cancer Classification Using Dermoscopic Images 2

PFCM Based Segmentation and TFA Based DCNN Model for Skin Ca...

引用

2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Manoj, H.M. Pawar, Priyanka Pramod Krupa, R. Pareek, Piyush Kumar Kumar, Deepak Bandeppa, Lalitha Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India University of the Cumberlands Information Technology Cumberlands United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Worldwide, skin lesions have emerged as a serious health concern;however, the survival rate can be improved by identifying skin lesions sooner with dermoscopy pictures. It will be a painstaking process to classify the skin lesion from those dermoscopy photographs. Using deep learning models improves the accuracy of skin lesion categorization. There has been a recent influx of convolutional neural networks (CNN) into this field;CNNs have well-established methods for feature extraction, which improves classification. This project aims to create AI-based solutions, namely deep learning (DL) procedures, to differentiate among benign and malignant skin lesions in dermoscopy pictures. Seven types of skin lesions were used in the Skin Cancer MNIST: HAM10000 dataset. Visual illness diagnosis using deep learning models is becoming increasingly common. For the purpose of skin cancer classification, this study suggests using a deep convolutional neural network (DCNN). In order to enhance the accuracy of the proposed DCNN classifier, the Tangent Flight Algorithm (TFA) is recommended as the optimum approach for optimising the weights, and possibilistic fuzzy clustering (PFCM) is employed for segmentation. Results from this study demonstrate that, compared to convolutional approaches, the suggested technology provides superior accuracy. The proposed TFA based DCNN model outperformed state-of-the-art prediction methods with training accuracy of 0.962, testing accuracy of 0.947, sensitivity of 0.824, and F1-score of 0.822. © 2024 IEEE.

关键词： Convolutional neural networks

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Innovative Horizons in Agricultural technology with TSA Based StrawberrySqueezeNet Classification Model 2

Innovative Horizons in Agricultural Technology with TSA Base...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Manoj, H.M. Pawar, Priyanka Pramod Krupa, R. Pareek, Piyush Kumar Kumar, Deepak Vindhya, G.B. Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India University of the Cumberlands Information Technology United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Channabasaveshwara Institute of Technology Tumkur Gubbi India

ISBN: (纸本)9798350373110

A fruit plant with sufficient value is the strawberry plant. High concentrations of fibre, antioxidants, potassium, vitamin C, and folic acid can be found in strawberry fruit. Because bacteria, pests, and microbes frequently impact strawberry plants, cultivating strawberries is a simple operation. Digital image processing will be used to introduce strawberry disease initially and limit its spread in strawberry plants. To improve strawberry disease diagnosis, a multi-pronged strategy was used, starting with the application of a carefully selected dataset. The dataset was then enriched and diversified using data augmentation techniques, which made it easier to build a reliable and complex model. For classification, the StrawberrySqueezeNet architecture was used, utilising its inherent capacity to identify complex patterns in the data. For hyperparameter tuning, a Tangent Search technique was used to maximise the model's performance. In order to get higher classification accuracy, the iterative optimisation procedure dynamically adjusted parameters as it explored the hyperparameter space. The result of these efforts was an astounding accuracy of 98.3%, which beat the industry standards set by other models. © 2024 IEEE.

关键词： Fruits

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Exploration Approaches for Identifying Key Mineral Resource Prospects 4

Exploration Approaches for Identifying Key Mineral Resource ...

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4th International Conference on Multimedia Processing, Communication and Information technology, MPCIT 2024

作者： Mantha, Girish Ashwini, J.P. Poornalatha, G. Manipal Institute of Technology Manipal Academy of Higher Education Department of Information and Communication Technology Manipal576104 India Department of Information science and Engineering Jawaharlal Nehru New College of Engineering Shivamogga India Jawaharlal Nehru New College of Engineering Artificial Intelligence & Machine Learning Department Shivamogga India

ISBN: (纸本)9798350375466

This study provides an in-depth review of mineral exploration methodologies, tracing the evolution from traditional approaches to the most recent advancements in machine learning algorithms. Over the past three to four decades, GIS-based methods have been widely used for mineral mapping, demonstrating substantial efficacy. However, the emergence of more advanced methodologies, particularly machine learning (ML) algorithms, has significantly enhanced computer-based mapping in mineral exploration. Notably, Random Forest-a prominent shallow ML algorithm-and Convolution Neural Networks (CNNs)-a key Deep learning approach-have emerged as powerful tools in this domain. This work highlights the application of ML-based algorithms for determining Lead and Zinc mineral compositions in a study region from which data is collected as in-situ analysis. All three algorithms are analyzed for effectiveness and accuracy. © 2024 IEEE.

关键词： Convolution Neural Network Decision Tree Deep learning K-Nearest Neighbor machine learning Mineral Mapping Support Vector machine

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