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Real Time Violence Detection System Using YOLOv7 and Deep learning Techniques

Real Time Violence Detection System Using YOLOv7 and Deep Le...

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

作者： Sudha Senthilkumar Gracy Agarwal Arshia Shirish Shruti Kolte School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Vellore India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In recent times, violence in public spaces such as schools, hospitals, and other community environments has increased a lot. Due to this alarming rise in violence CCTV cameras have been deployed everywhere to enhance safety and security. However, these systems depend on manual monitoring which is prone to human errors and cause delays in the identification of violent events and action which can lead to significant damage. To overcome these limitations, this study has proposed a violence detection and alarm system based on deep learning algorithms. The hybridization of YOLOv7 allows rapid recognition of possible threats by identifying human presence and movement, and the lightweight classifier provides a high accurate classification of the identified tasks while reducing the processing computational resources of the system. With this dual-model-based framework, it is guaranteed that both the speed and accuracy can be achieved, and that the system can be implemented on edge devices such as mobile devices. The function of the system is to identify violence while it is happening and to issue alerts to security or law enforcement authorities committed to rapid response. The system was tested in real-time scenarios and achieved 91% accuracy in detecting violent actions. The system incorporates a Telegram bot to send real-time alerts to the relevant authorities. By leveraging advanced deep learning techniques, this study seeks to close the gap between existing surveillance methods and the need for a more automated and accurate violence detection system.

关键词： Deep learning Performance evaluation Visualization Accuracy Surveillance Image edge detection Streaming media Real-time systems Safety Usability

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IoT-Driven intelligent Trash Bin (IntelliBIN) Enabled with Edge Segregation for Malls

IoT-Driven Intelligent Trash Bin (IntelliBIN) Enabled with E...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： D HarshaVardhan Reddy T Subhash Gupta C Gowthami Reddy S Vishnu R Vishnuvarthan B Sree Darshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Urban waste management, especially from commercial sources like shopping malls, presents a significant challenge. A large amount of packaging waste generated by retail outlets and food courts complicates the waste management process. The current method of segregating waste at centralized facilities leads to time-consuming processes, increased transportation costs, and increased labor demands. In response, this work proposes an innovative solution: an IoT-Driven intelligent Trash Bin with Edge Segregation for shopping malls. By deploying a deep learning model on a Raspberry Pi board, waste segregation is optimized at the edge, reducing latency and improving efficiency. The system includes an intelligent bin named IntelliBIN, featuring four compartments, and utilizes the proposed deep learning model to analyze and segregate the wastes into paper, metal, food, and others via a servo mechanism. In addition, the bin is equipped with ultrasonic sensors to monitor the filled levels of each compartments and a Wi-Fi radio device to enable real-time data transmission and monitoring. The statistical data of waste disposal is presented, and automated notifications are sent to the mall management to take appropriate action. The system assures effective waste segregation and management with the proposed model by achieving a training accuracy of 94%, and it outperforms some of the existing works such as VGG 19 (92%), ResNet (93%), and Inception Net (87%).

关键词： Deep learning Training Accuracy Metals User interfaces Servomotors Waste disposal

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Potato disease detection using machine learning 3

Potato disease detection using machine learning

引用

3rd international conference on intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Tarik, Marjanul Islam Akter, Sadia Mamun, Abdullah Al Sattar, Abdus Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9780738111834

In Bangladesh potato is one of the major crops. Potato cultivation has been very popular in Bangladesh for the last few decades. But potato production is being hampered due to some diseases which are increasing the cost of farmers in potato production. However, some potato diseases are hampering potato production that is increasing the cost of farmers. Which is disrupting the life of the farmer. An automated and rapid disease detection process to increase potato production and digitize the system. Our main goal is to diagnose potato disease using leaf pictures that we are going to do through advanced machine learning technology. This paper offers a picture that is processing and machine learning based automated systems potato leaf diseases will be identified and classified. Image processing is the best solution for detecting and analyzing these *** this analysis, picture division is done more than 2034 pictures of unhealthy potato and potato's leaf, which is taken from openly accessible plant town information base and a few pre-prepared models are utilized for acknowledgment and characterization of sick and sound leaves. Among them, the program predicts with an accuracy of 99.23% in testing with 25% test data and 75% train data. Our output has shown that machine learning exceeds all existing tasks in potato disease detection. © 2021 IEEE.

关键词： Machine learning

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Sickle Cell Classification Using Deep learning

Sickle Cell Classification Using Deep Learning

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intelligent Technologies (CONIT), international conference on

作者： Neelankit Gautam Goswami Anushree Goswami Niranjana Sampathila G. Muralidhar Bairy Department of Biomedical Engineering Manipal Academy of Higher Education (MAHE) Manipal Institute of Technology Manipal School of Biotechnology and Bioinformatics D.Y. Patil Deemed to be University Navi Mumbai

Artificial Intelligence has been a boon to healthcare for quite a long time. While AI has the potential to assist in several domains, blood smear analysis has several challenges that need to be addressed to ensure the accuracy, interpretability, safety, and ethical use of AI in this context. Proper validation, overlapping of cells, data availability, addressing biases, interpretability, regulatory compliance, workflow integration, and ethical considerations are important aspects that must be carefully considered when using AI in blood smear analysis. One of the lesser-tackled problems through Artificial Intelligence is the classification of sickle cells. Sickle cell disease is a genetic disorder affecting the hemoglobin resulting in a reduced supply of oxygen to the entire body. Currently, there is no cure for sickle cell disease, and treatment is focused on managing symptoms and preventing complications. Manual identification and classification of sickle cells in blood smear images can be time-consuming and prone to human error. Hence, there is a need for automated methods to classify sickle cells and tackle these problems. This paper discusses a deep learning model to detect the presence of sickle cells in the blood, thereby classifying them. The model focused on here is ResNet50 giving a test accuracy of 93.88%.

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Detection of Brain Tumors from MRI Images using Novel Deep CNN

Detection of Brain Tumors from MRI Images using Novel Deep C...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Mohebbanaaz P. Deepthi Jordhana G Vamsi Krishna Dept of ECE Ravindra College of Engineering for Women Kurnool Andhra Pradesh India Dept of ECE PVKK Institute of Technology Anantapur Andhra Pradesh India Department of EIE VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Brain tumors detection is the most crucial aspects in neuro-oncology. Early detection of tumors can significantly prevent critical health issues, improving the survival rates. Magnetic Resonance Imaging (MRI) has proven to be a effective tool in diagnosing brain tumors. This paper presents a deep learning (DL) methodology for the detection of brain tumors using Convolutional Neural Networks (CNNs). Given the complexity and high dimensionality of medical imaging data, transfer learning allows us to leverage pre-trained architecture as backbone and accelerate the process of training. The developed architecture is further is fine-tuned with different optimizing parameters. Our proposed approach achieved an accuracy of 98.70%. Experimental results demonstrate the success of developed models in detecting tumors with high accuracy, offering a promising solution for automated medical diagnostics.

关键词： Training Accuracy Magnetic resonance imaging Brain tumors Computer architecture Brain modeling Real-time systems Convolutional neural networks Medical diagnosis Medical diagnostic imaging

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Implementation of a Human-Following Smart Trolley Using RFID with automated Product Scanning

Implementation of a Human-Following Smart Trolley Using RFID...

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

作者： Ramasubramanian G Suvarna Smita Raja Teja V Dixit Sheethal R Sri Balaji S Sivadharishana T D Dept. AIML Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Shopping convenience and automation have gained much priority in the retail industry, especially with the advent of RFID and IoT. This paper presents a Human-Following Smart Trolley using RFID, together with automated product scanning, to remove general shopping hassles in the form of manual handling of a cart and delay during checkout. It proposes the integration of RFID technology for authentication, real-time scanning of products, ultrasonic sensors for tracking humans autonomously, and a microcontroller-based system to handle all operations together. This trolley follows the user at a distance and detects the items that are placed inside it with the help of an RFID tag. The overall bill is projected to calculate the value of the LCD display. The implementation demonstrates the capability of the system to work autonomously in dynamic retail environments for accurate billing and efficient navigation. It cuts down shopping time while increasing customer convenience. The system design is modular; hence, it is scalable and easy to adapt for future improvements. Besides assured accuracy, the system is cost-effective, with Arduino microcontrollers, RFID modules, and ultrasonic sensors fairly inexpensive and hence suitable for small to mid-scale retail setups. The present study investigates how the combination of automation and smart technologies is enabling the redefinition of the retail shopping experience. Further development will likely focus on obstacle avoidance, machine learning for personalization, and full checkout-free capability through mobile payment integrations that will characterize smarter, efficient retail automation in the years to come.

关键词： Automation Accuracy Scalability Acoustics Real-time systems Sensor systems Solar panels Wearable devices intelligent sensors Radiofrequency identification

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Hate speech detection in twitter using natural language processing 3

Hate speech detection in twitter using natural language proc...

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3rd international conference on intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Pariyani, Bhavesh Shah, Krish Shah, Meet Vyas, Tarjni Degadwala, Sheshang Nirma University Data Science Ahmedabad Gujarat380060 India Sigma Institute of Engineering Department Computer Engineering

ISBN: (纸本)9780738111834

Twitter's central goal is to enable everybody to make and share thoughts and data, and to communicate their suppositions and convictions without boundaries. Twitter's job is to serve the public discussion, which requires portrayal of a different scope of points of view. Yet, it does not advance viciousness against or straightforwardly assault or undermine others based on race, nationality, public cause, rank, sexual direction, age, inability, or genuine illness. Hate Speech can hurt a person or a community. So, it is not appropriate to use hate speech. Now, due to increase in social media usage, hate speech is very commonly used on these platforms. So, it is not possible to identify hate speeches manually. So, it is essnetial to develop an automated hate speech detection model and this resaech work shows different approaches of Natural Language Processing for classification of Hate Speech through Machine learning Algorithms. © 2021 IEEE.

关键词： Decision trees

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Reverse engineering the Hamming Code with Automatic Graph learning 3

Reverse Engineering the Hamming Code with Automatic Graph Le...

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3rd international conference on Artificial Intelligence in Information and Communication (IEEE ICAIIC)

作者： Jacobsen, Noah B. Mission Solut Grp Inc Data Sci N Charleston SC 29406 USA

ISBN: (纸本)9781728176383

Probabilistic graphical models arc used extensively across the information theory, artificial intelligence and machine learning disciplines. In this paper, we work towards realizing a generalized graph-based framework for automated learning in intelligent systems. The proposed automatic graph learning framework employs factor graphs. i.e. Tanner graphs from coding theory. to represent an arbitrary stochastic system of variables and factorized realizations of their joint probability density function. We develop algorithms that are capable of learning statistical relationships between system variables, which involves constructing an appropriate factor graph representation and generating estimates of its component probability density unctions, from training data. In this paper, automatic graph learning is used to reverse engineer the Hamming code, based on training data comprised of input-output codeword pairs. We show that automatic graph learning is capable of replicating known decoder performance with an order of magnitude less training data than a multi-layer dense neural network.

关键词： learning theory probabilistic graph models factor graphs Bayesian and neural networks probabilistic inference sum-product algorithm

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Detection and Classification of Eye Diseases using Hybrid Deep Features with Decision Tree Algorithm

Detection and Classification of Eye Diseases using Hybrid De...

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Advances in engineering Science and Technology (AEST), international conference on

作者： Ahmed Aizaldeen Abdullah Ahmed Aldhahab Hanaa Mohsin Al Abboodi Department of Electrical Engineering University of Babylon Babylon Iraq

ISBN: (数字)9798331502690

ISBN: (纸本)9798331502706

In ophthalmic diagnostics, it is essential to identify certain ocular conditions in fundus images. This work presents the Decision Tree (DT) algorithm, an automated multi-label deep learning classification system that integrates deep feature representations from pairs of fundus images (e.g., left and right eyes) to diagnose multiple ocular disorders. The benchmark structured fundus image dataset Ocular Disease intelligent Recognition (ODIR-5K) has been used to classify fundus images into normal, diabetes, glaucoma, cataract, age-related macular degeneration, hypertension, pathological myopia, and others. Our suggested approach consists of three steps: pre-processing, deep feature extraction based on transfer learning concepts, and classification using a classical decision tree classifier. The study initiates a comprehensive image pre-processing procedure, including circular border cropping, image resizing, data balancing, and data augmentation. The work aims to categorize a dataset of eye diseases using DT and Hybrid Features (HF) based transfer learning models. This approach has been applied to identify the eye disease dataset based on fused characteristics from the EffiientB6 and Densnet169 models as transfer learning models. The final classifier (Decision Tree) achieved an F1-score of 99.02%, accuracy of 99.02%, precision of 99.04%, and recall of 99.01%.

关键词： Macular degeneration Hypertension Pathology Image recognition Transfer learning Hafnium Feature extraction Classification algorithms Decision trees Diseases

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VGG16-Based Deep learning Approach for Accurate Detection of Fruit Diseases in Precision Agriculture

VGG16-Based Deep Learning Approach for Accurate Detection of...

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

作者： S. N. Sheela Evangelin Prasad Nitin Rakesh Elavel Visuvanathan G N. Mohankumar R. Vinodha S. Murugan Department of Computer Science and Engineering Sri Krishna Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics and Communication Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Early and accurate disease detection in fruit is crucial for precision agriculture to improve yields and provide healthy crops. This research proposes a deep learning (DL) method based on VGG16 to detect fruit illnesses from high-resolution images. The method uses VGG16 architecture, which has been fine-tuned using massive image datasets, to detect fruit illnesses. The proposed technique takes images of fruits that have several prevalent illnesses and uses those images to extract deep characteristics that allow for accurate classification. This system accurately identifies illnesses, including apple scab, citrus greening, and bacterial spots, by combining a strong convolutional neural network (CNN). The dataset used to train and test the model to simulate real-world situations contains images of healthy and sick apples taken under different lighting circumstances. The evaluation of performance indicators, including recall, accuracy, precision, and F1-score, shows the model's efficiency and accuracy in illness detection. Reduced pesticide consumption and optimized resource allocation result from this automated detection system's ability to allow farmers to monitor crop health and make prompt choices continually. It provides a useful and scalable tool for managing diseases in modern precision agriculture. A DL model based on VGG16 was created for accurate fruit disease diagnosis in precision agriculture, with an accuracy of 96.5%, allowing early intervention to improve crop health and production sustainability.

关键词： Precision agriculture Deep learning Accuracy Crops Production Convolutional neural networks Resource management Sustainable development Monitoring Diseases

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