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Detecting Criminal Activities From CCTV by using Object detection and machine Learning Algorithms

Detecting Criminal Activities From CCTV by using Object Dete...

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Intelligent Technologies (CONIT), international conference on

作者： Surbhi Singla Raman Chadha CSE AIML University Institute of Engineering Chandigarh University Mohali India CSE University Institute of Engineering Chandigarh University Mohali India

Now, a day’s Crime in every country is increasing day by day. Generally, Every day we listen to the news of different crimes of different categories like rape, assault, Kidnapping ,Robbery ,ATM Theft, Murders etc happening in different states, cities , countries. Almost all the newspapers, TV channels’, social media are filled with the news of Criminal activities happening all around the Whole World. In earlier times there is no method to detect Crime. After That the CCTV cameras were used to detect Crimes. But Watching these Videos manually by humans for detecting crimes is a very time Consuming process especially in today’s world of Artificial intelligence and Machine learning .So this crime detection in CCTV surveillance becomes an important area of research in the field of machine learning. So, there is a very urgent need of the intelligent system which will detect the crimes from the real time CCTV Feed and classify them and provides an alert system to the nearest police stations and ambulances etc. So, that system will help in reducing the crime rate in any country. This paper reviews all prior research in this area, including approaches for object recognition and finding priority frames, techniques and algorithms like Yolo used to detect crimes , various datasets used and algorithms used to analyze crime data and train the dataset .It covers the various recent trends in researches in this field and analyzing the challenges faced and various research gaps and this paper also discuss how we can overcome these gaps in research so as to develop a better intelligence surveillance system in ml field.

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Machine Learning Approaches for Improving the Accuracy of Blood Cell detection and Subtypes Classification Using Smear Microscopic Images 3rd

Machine Learning Approaches for Improving the Accuracy of Bl...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Raja, S. Pravinth Khan, Sameeruddin Bhatnagar, Shaleen Chen, Thomas M. Sathiyanarayanan, Mithileysh Department of CSE Presidency University Bangalore Bengaluru India City University of London London United Kingdom School of CSE and IS Presidency University Bangalore Bengaluru India Department of CSE Manipal University Bengaluru India School of Science and Technology City University of London London United Kingdom MIT Square London United Kingdom

ISBN: (纸本)9789819726707

The human blood contains red blood cells (RBC), white blood cells (WBC), platelets, and plasma. The entire blood cell count defines the state of health. A normal human has RBCs ranging from 4.5 to 6.0 million cells per microliter in males and 4.0–5.0 million cells per microliter in females, as well as WBCs ranging from 4.5 to 11.0 thousand cells per microliter in both males and females. The segmentation and identification of blood cells are extremely vital. The RBC and WBC counts are extremely important to diagnose varied diseases such as haemolytic anaemia, nutritional anaemias, acute myeloid leukaemia, chronic myelogenous leukaemia, and chronic lymphocytic leukaemia. The count of blood cells is performed in manual hospital laboratories using a victimisation device known as a hemocytometer and magnifier. However, this technique is tedious, laborious, and time-consuming, and it produces incorrect results due to human error. Also, there are some expensive machines, like instruments, that do not seem to be reasonable in each laboratory. The proposed method has involved the use of image processing to classify the blood cells with the help of ResNet deep neural networks. The algorithm can extract the feature of each segmented cell image and classify the types. The overall accuracy was 93.01%. The system has been developed to provide accurate and fast results using large dataset of blood smear images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Potato Plant Leaf Disease detection and Recognition Using R-CNN Model

Potato Plant Leaf Disease Detection and Recognition Using R-...

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Minu Balakrishnan Raja M P.Vinoth Kumar Suvitha Subramaniam Latha R Department of Information Technology Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Depatment of Mechatronics Chennai Institute of Technology Chennai India Department of English KPR Institute of Engineering and Technology Coimbatore India Saveetha school of Engineering Saveetha Institute of Medical and Technical Science- (SIMATS) Chennai India

A smart farming system that makes use of the appropriate infrastructure is an example of a new technology that aids in the improvement of the quality and quantity of the country's agricultural goods, such as potatoes. Diseases cannot be prevented since developing potato plants requires consideration of factors such as the environment, soil, and quantity of sunlight. Deep learning has resulted in recent advances in computer vision. These advancements have enabled Potato to utilize a camera to diagnose disease. This study developed an effective new method for detecting infections in potato plants. Yukon Gold, a kind of potato, served as the trial subject. Specifically, Alternaria, Blackleg, and Target Spot were targeted for detection using this method. Using 5,615 images of diseased and healthy Potato plant leaves taken in controlled environments, deep CNN is trained to identify between the presence and absence of three illnesses. To identify the Potato illnesses affecting the tracked plants, the system used a CNN. Anomaly detection using the F-RCNN-trained model achieved a confidence score of 80%, while illness identification using the Transfer Learning model achieved an accuracy of 97.85%. The effectiveness of the automated image-capturing system for diagnosing diseases in Potato plant leaves was determined to be 93.33%.

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Learning State detection with Multimodal Information in Virtual Reality Learning

Learning State Detection with Multimodal Information in Virt...

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Digital Twins and Parallel intelligence (DTPI), IEEE international conference on

作者： Hongxia Zhao Siyu Bian Xiwei Liu Sifeng Jing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao China

The learning state is a crucial factor that significantly impacts students' learning efficiency and overall learning outcomes. It also serves as a vital indicator for evaluating teachers' effectiveness in their instructional practices. To assess the learning state, various aspects of students' behavior, including their expressions, actions, and language, can effectively reveal valuable insights. Traditionally, studies have focused on investigating the learning state in conventional classroom and online education settings. However, limited research has been conducted in the context of virtual reality (VR) immersive learning environments. Considering the unique challenges posed by VR, such as the occlusion of the upper part of the face by VR devices and the distinct characteristics of VR interaction, we propose a novel method for detecting the learning state while wearing VR during the learning process. This method integrates multimodal information, including video captured by a camera, eye movement sensor data from the VR device, and electroencephalography (EEG) signals from an EEG headband. By comprehensively analyzing these inputs, we can determine the learning state effectively. An experimental evaluation was conducted to validate the effectiveness of the proposed multimodal information infusion learning state detection method in the context of VR immersive learning. The results demonstrate that this approach significantly enhances the accuracy of determining the learning state. Ultimately, this advancement has the potential to improve the overall learning quality and facilitate the widespread adoption of VR in educational settings.

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detection of Counterfeit Currency Notes Through Machine Learning Algorithms and Image Processing

Detection of Counterfeit Currency Notes Through Machine Lear...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Vikrant Shokeen Sandeep Kumar Amit Sharma Neha Singh Aman Prasad Yugank Sachdeva Maharaja Surajmal Institute of Technology New Delhi India IMS Engineering College Ghaziabad UP India Maharaja Agrasen Institute of Technology New Delhi India

In the modern financial sector, identifying fake money is a constant struggle. In order to address this issue, this research introduces a novel technique that correctly and quickly detects fake certifications by fusing machine learning methods with image processing. The suggested method trains a machine learning model with information from photos of actual and fake banknotes so that it can recognize the distinctive characteristics and patterns that separate real and fake banknotes. Additionally, different image processing techniques are employed to enhance image quality, lessen noise, and boost the clarity of crucial features. The efficiency of the procedure and accuracy in identifying fake goods were tested using the results. The proliferation of counterfeit certifications can be stopped, safeguarding the economy and boosting public confidence in the system, with the help of machine learning algorithms and machine *** of fake credentials, safeguarding the economy and boosting public confidence in the system.

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Siamese Network Based Anomaly detection Framework for Robust Federated Learning

Siamese Network Based Anomaly Detection Framework for Robust...

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Intelligent Technologies (CONIT), international conference on

作者： Karthik Shenoy K Manohara M. M. Pai Radhika M. Pai Department of Information and Communication Technology Manipal Institute of Technology Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal India

Federated learning is a decentralized approach to machine learning that has increased in popularity in recent years. It enables several participants to train a common model without revealing their data. This strategy is, however, susceptible to attacks from malicious clients who can launch targeted model poisoning attacks and reduce learning performance by delivering false model updates to the server. It is necessary to identify and eliminate such fraudulent updates and the attackers behind them to preserve the robustness and security of the shared model. In this paper, a novel Siamese network-based architecture for robust federated learning is proposed. which can identify and eliminate harmful updates. Our method is assessed and compared with other approaches for adversarial detection in image classification tasks in a federated setting, using a CNN model. Experimental findings demonstrate that the system offers reliable federated learning that is resistant to both targeted model poisoning and untargeted Byzantine attacks. Overall, the study advances the development of secure federated learning systems, by suggesting a novel method for identifying and deleting fraudulent updates in federated learning,

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Waste Management detection Using Deep Learning

Waste Management Detection Using Deep Learning

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Computing and Information technology (ICCIT-1441), 2020 international conference on

作者： Pokkuluri Kiran Sree Phaneendra Varma Chintalapati SSSN Usha Devi N M. Prasad Gurujukota Ramesh Babu P.B.V. Raja Rao Dept of C.S.E Shri Vishnu Engineering College for Women(A) Bhimavaram University College of Engineering JNTUK Kakinada

In the modern world waste pollution is the challenging environmental issues. To reduce pollution, we need to recycle the waste. In order to recycle waste, one must know which category the waste belongs to. Waste classification is a social issue related to people’s livelihood, so letting smart systems perform intelligent waste classification has important significance. So, we are here with a waste detection system that uses deep learning and categorizes the waste plastic or non-recyclable or glass or bio or others. The proposed system is trained and tested to detect the classify the waste using the CNN(Convolution Neural Network) i.e. a deep learning algorithm. The image of the waste material is fed into a neural network which determines the class of object. We have considered precision, recall and F1 Score as performance metrics to evaluate our model. The proposed model is compared with existing baseline models like machine learning, basic neural networks and datamining approach. The developed deep learning model has outperformed the existing literature with chosen parameters.

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Designing a Deep Learning Model for recommendation of Drugs on Sentiment Analysis

Designing a Deep Learning Model for recommendation of Drugs ...

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Mysore Sub Section international conference (MysuruCon), IEEE

作者： T M Aruna T L Nethravathi M Muniraju Dept of AI-ML Nitte Meenakshi Institute of Technology Bengaluru Department of Artificial Intelligence and Machine Learning SJCIT Chickballapur

There has been a rise in creative automation work in recent years, and various applications have been found for machine learning. This essay's focus is on introducing a pharmaceutical recommendation system with the potential to greatly reduce the burden on experts. With the help of a deep learning algorithm, we create a drug recommendation system that utilises patient reviews to predict sentiment through the use of a number of vectorization techniques. Synthetic minority over-sampling method (SMOTE) is used to rectify the effects of unbalanced data on classification accuracy. The evaluation is sorted using a Recurrent Neural Network (RNN), with the weights chosen using an enhanced version of the firefly optimisation algorithm (IFFOA). Predicted feelings were evaluated using a battery of metrics counting precision, recall, f1score, accuracy, and specificity score. The classifier RNN-FCR has a higher accuracy rate (96%) than any other models as a direct result of TF-IDF vectorization.

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UAV Target detection and Path Planning Based on Improved YOLOv4 Networks with Improved Artificial Potential Field Methods

UAV Target Detection and Path Planning Based on Improved YOL...

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Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Zhanhan Liu The Experimental High School Attached to Beijing Normal University Beijing China

In recent years, with the rise of the application of deep learning tasks in the end of multi-rotor UAVs, it is a very scientifically meaningful and practical research to combine artificial intelligence technology with pattern recognition technology to realize automatic target detection and autonomous obstacle avoidance in UAVs. The traditional manual inspection method has the problems of low efficiency, high labor and material consumption and low safety, so the country is vigorously developing the drone industry at this stage. UAV inspection tasks are becoming more and more important, and it has become a trend for UAVs to shift to the direction of unmanned operations. With the increase of UAV density in large cities, it is meaningful to control and manage the trajectory of UAVs and provide intelligent trajectory optimization and anti-collision system. In order to achieve safe and fast autonomous flight, it is necessary to intelligently optimize the flight trajectory of UAV to ensure the best response time and increase the duration of continuous flight. At the same time, due to the low-slow-small characteristics of UAVs and the complexity of cities, UAVs are often faced with the interference of airborne obstacles, such as utility poles or flying birds, and need to perform real-time environment sensing and change their trajectories in time to avoid these obstacles. We propose an ED-YOLO network model for target detection of UAV obstacles. The target detection algorithm is based on YOLOv4, and firstly, a channel attention mechanism is added to the backbone network to improve the detection accuracy without increasing the computation. Compared with YOLOv4, its average accuracy mean of obstacle target detection is only reduced by 1.4%, while the model volume is reduced by 94.9%, the floating-point operation volume is reduced by 82.1%, and the prediction speed is improved by 2.3 times. We also studied UAV obstacle avoidance path planning. Firstly, we propose the UAV obstac

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An Efficient Approach of Drowsiness detection Based on ResNet50 and Xception Architectures

An Efficient Approach of Drowsiness Detection Based on ResNe...

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Electronic Engineering (ICEEM), international conference on

作者： Samy Abd El-Nabi El-Sayed M. El-Rabaie Ahmed Emam Khalil F. Ramadan Dept. of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University (KSIU) South Sinai Egypt Dept. of Electronics and Electrical Communications Faculty of Electronic Engineering Menoufia University Menouf Egypt Dept. of Computer Science and Math Faculty of Science Menoufia University Menoufia Egypt

This paper delves into a critical aspect of road safety by investigating a method to detect drowsiness in images. The accurate utilization of this technology by fatigued drivers holds the potential to prevent numerous fatal accidents, as the proposed models exhibit swift responsiveness in identifying instances of driver drowsiness. Within the realm of eye closure detection, two drowsiness models, namely ResNet50 and Xception, are employed to portray various eye state classifications. Nonetheless, the scarcity of readily available and reliable eye datasets poses a formidable challenge. Leveraging the power of ResNet50, deep facial features are extracted, resulting in an impressive accuracy of 98.06%. Similarly, the utilization of Xception yields a commendable accuracy of 95.69% when evaluated with the MRL dataset. These experimental outcomes firmly establish the higher accuracy and cost-effectiveness of eye closure estimation, thereby underscoring the efficacy of the proposed framework for drowsiness recognition.

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