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A Survey on Surveillance using Drones 2022

A Survey on Surveillance using Drones

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14th International Conference on Contemporary Computing, IC3 2022

作者： Sai Theja, Golla Uday Murari, Motukoori Sai Singha, M. Franckie Patgiri, Ripon Choudhury, Ananya Dept. Electronics and Communication Engineering National Institute of Technology Silchar Silchar Assam Cachar India Dept. Computer Science and Engineering National Institute of Technology Silchar Silchar Cachar India Department of Radiation Oncology Grow School for Oncology and Reproduction Maastricht University Medical Center Netherlands

ISBN: (纸本)9781450396752

Drones are flying robots that can be remotely controlled. In this paper, we discussed different ways a drone can be used for surveillance. Drones are used to detect forest fires, manage traffic remotely, surveillance of crowds, etc. Drones make surveillance effective, accessible, and time-saving. Drones surveillance dearth the researcher's conscientious, and therefore, we accumulate the associated literature and converse their realistic standpoint in general. This paper makes a specialty of video surveillance, the usage of drones in item detection and tracking, video summarization, continual tracking of the quarry, seek and rescue related operations in an adverse environment, traffic control in smart towns, and catastrophe control in a direful situation. This quick survey lock-up mild at the studies gaps. © 2022 ACM.

关键词： Object detection

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Securing Internet of Vehicles Protocols using ASCON and GIFT-COFB

Securing Internet of Vehicles Protocols using ASCON and GIFT...

引用

IEEE Conference on Vehicular technology (VTC)

作者： Wissal BenMassaoud Darshan M Rutvij H. Jhaveri Gautam Srivastava Dept. of Computer Science Lakehead University Thunder Bay ON Canada Dept. of Computer Science and Engineering School of Technology Pandit Deendayal Energy University India Dept. of Math and Computer Science Brandon University Brandon MB Canada

The Internet of Vehicles (IoV) is a network of vehicles that are connected through the Internet using communication protocols. However, this raises concerns about security and privacy. The vulnerability of object networks has been highlighted in recent reports on cybernetics, and attacks on these networks could pose significant dangers to data integrity. To avoid this, security measures such as resistance to attacks, data authenticity, access control, and privacy must be integrated. The limited capabilities of IoT objects in terms of processing power, memory, bandwidth, and energy have led to the creation and redesign of protocols and architectures that meet the specific needs of these networks. One such protocol is the Message Queue Telemetry Transport (MQTT), which uses a Publish/Subscribe methodology and small amounts of bandwidth. To address security concerns, the paper proposes using the lightweight cryptographic techniques ASCON and GIFT-COFB, which could be useful in securing communication and data exchange in IoV sensor networks.

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Moving Robots in Unknown Environments Using Potential Field Graphs 2

Moving Robots in Unknown Environments Using Potential Field ...

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2nd International Conference on Advanced Research in Computing, ICARC 2022

作者： Panagoda, Mithila Lokuliyanage, Minuli Senarath, Amadi Nisansala, N.K.V.M. Nethmini Rajapakshe, R.W.A.D.U. Rajapaksha, U.U. Samantha Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dept. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Deprt. of Computer System and Engineering Malabe Sri Lanka

ISBN: (纸本)9781665407410

The purpose of this research paper is to introduce a new navigation algorithm for Robot Operating System (ROS) based robots which will allow complete autonomous traversal in any given indoor environment. Turtle bot3 burger bot is the sample robot chosen for this project. This will be equipped with a Light Detection and Ranging (LIDAR) scanner with the default settings, ultrasound, Intel Real Sense camera, etc. Environment information gathered via these sensors will be sent into a remote computer for processing since the raspberry pie micro-controller with the turtle bot burger is not enough to carry out a high computation-intensive task. Once the information is received series of tasks will be carried out by the remote personal computer before executing the newly developed navigation algorithm. The data from the LIDAR scanner help to generate a 2-dimensional(2D) potential field graph. Then it will later be used for path planning by navigation algorithm. Furthermore, the Real sense camera is used for object boundary detection to produce more accurate data for the navigation algorithm. Once all the data proceed, data will return to the robot, where the navigation algorithm will start execution in parallel with a recovery behavior algorithm and scan algorithm. The recovery behavior algorithm is responsible for guiding the robot to face away from an obstacle in an event where the predetermined path is obstructed. Furthermore, it is responsible for maintaining accurate data in the cost map. Subsequently, the scanning algorithm will be used to generate maps. © 2022 IEEE.

关键词： Cameras

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A Computational Analysis of Optimization Techniques to Solve Assignment Problem

A Computational Analysis of Optimization Techniques to Solve...

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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： Yash Kuma Ramesh Ch. Sahoo Prashant Dixit Dept.of Computer Science & Engineering Manav Rachna International Institute of Research and Studies Faridabad Haryana India Dept. of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India

ISBN: (数字)9798331541217

ISBN: (纸本)9798331541224

In the field of combinatorial optimization, numerous methods have been developed to address complex problems, including assignment, scheduling, and resource allocation. This paper presents a comparative analysis of seven prominent optimization techniques: Hungarian Algorithm, Greedy Algorithm, Auction Algorithm, Linear Programming, Genetic Algorithm, Simulated Annealing, and Constraint Programming. We evaluate these methods on computational efficiency, scalability, and practical applicability, focusing on assignment problems. The analysis reveals that Hungarian Algorithm, Linear Programming, and Constraint Programming consistently deliver high accuracy across all matrix sizes, making them ideal for scenarios where solution precision is paramount. Conversely, the Greedy Algorithm excels in speed on smaller matrices but suffers from significant time and accuracy issues as matrix size increases. Additionally, while both Constraint Programming and Genetic Algorithm achieve high accuracy, their execution times rise considerably with matrix size, especially for Genetic Algorithm, which becomes impractical for large-scale problems. Thus, Hungarian Algorithm, Linear Programming, and Constraint Programming are optimal for accuracy-focused tasks, while Greedy Algorithm is preferable for smaller, speed-critical cases.

关键词： Constraint handling Greedy algorithms Accuracy Processor scheduling Scalability Simulated annealing Linear programming Resource management Optimization Genetic algorithms

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Real-Time Sign Language to Speech Converter Using OpenCV and MediaPipe

Real-Time Sign Language to Speech Converter Using OpenCV and...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Anvisha Pathak Niraj Patil Prashant Padhy Adarsh Jadhav Smita Rukhande Lakshmi Gadhikar Dept. of Information Technology Fr. Conceicao Rodrigues Institute of Technology Navi Mumbai India Dept. of Computer Engineering Fr. Conceicao Rodrigues Institute of Technology Navi Mumbai India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Communication barriers between deaf individuals and hearing people can lead to several significant problems such as social integration, reduced access to information and reduced opportunities thereby affecting quality of life of deaf individuals. Use of sign language for communication serves as the primary means to address these challenges. However, understanding of the sign language is very difficult for hearing individuals. To address this issue, we present a Real-Time Sign Language to text and Speech Converter to facilitate effective communication between deaf and hearing individuals. This paper presents a novel approach to sign language recognition using OpenCV and MediaPipe. The main aim of the paper is to develop real time computer vision that can translate sign language gestures into text in real-time. We use image processing techniques to segment and analyze sign gestures, OpenCV to record and process these gestures, MediaPipe for hand tracking and gesture prediction and a text-to-speech engine to translate the text into speech for auditory communication. Thus, this study offers a comprehensive sign language to speech converter system that has the potential to significantly improve communication between the deaf community and the hearing people.

关键词： Hands Sign language computer vision Translation Text recognition Auditory system Speech recognition Real-time systems Text to speech Speech processing

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SeaBreeze Study: Exploring Air Quality Dynamics in Western Coastal Mumbai

SeaBreeze Study: Exploring Air Quality Dynamics in Western C...

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Communication & Computing (WCONF), World Conference on

作者： Aashish Jha Andrea Alvares Shrikrishna Umbare Amiya Kumar Tripathy Sanjay Shitole Dept. Of Computer Engineering Don Bosco Institute Of Technology Mumbai India Dept. of Information Technology Usha Mittal Institute of Technology SNDT Women's University Mumbai India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Coastal regions, surrounded by water are frequently thought of as refuges with perfect air quality which creates an interesting paradox. In particular, areas like Juhu and Raj Bhavan in Mumbai, known for their coastal attributes, exhibit a puzzling inconsistency in air quality data. While temperature and wind speed data from local weather stations follow expected trends, air quality measurements reveal significant and unexpected variations. the work presented in this paper centers on the development of an integrated system designed to facilitate accurate weather parameter forecasting and secure data access, with an unwavering commitment to user experience and data integrity. The primary objectives of the research are to determine the factors that influence the variations in both regions and to create a cohesive framework that seamlessly combines user-friendly front-end features with a secure and efficient back-end infrastructure, granting users effortless access to a diverse range of weather parameters for analysis. The methodology entails the deployment of various essential modules, including user authentication, front-end dashboard construction, RESTful API integration, back-end database management, predictive modeling, and secure data transmission

关键词： Temperature measurement Temperature distribution Accuracy Wind speed Prevention and mitigation Sea measurements Restful API Air quality User experience Meteorology

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Face Recognition Model Based on MTCNN and Facenet 2

Face Recognition Model Based on MTCNN and Facenet

引用

2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Qi, Siyao Zuo, Xinyu Feng, Weijia Naveen, I.G. Tianjin Normal University College of Computer and Information Engineering Tianjin China Nitte Meenakshi Institute of Technology Dept. of ECE Bengaluru India

ISBN: (纸本)9781665491112

Face recognition technology is widely used in various fields, such as time and attendance, payment, access control, etc., providing great convenience to life. This paper proposes a face recognition model based on MTCNN and Facenet, as traditional face recognition systems mostly use manual feature setting, which has disadvantages such as low recognition accuracy and slow speed. The MTCNN model consists of three layers of convolutional neural networks, namely P-Net, R-Net and O-Net, which are used to extract faces from images. The Facenet model is used for face feature vector extraction, and Triplet Loss is used as the loss function to determine the similarity of features through the comparison of Euclidean spatial distances. The final face recognition was completed. The experiments were trained using the LFW dataset and achieved an accuracy of 86.0%. © 2022 IEEE.

关键词： Face recognition

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Explainable Deep Learning for Lung Cancer Detection: Comparing CNN and DenseNet201 with Grad-CAM

Explainable Deep Learning for Lung Cancer Detection: Compari...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Sarvagya Kaushik Amanjot Kaur Lamba Isha Kansal Vikas Khullar Preeti Sharma Dept. of Computer Science and Design Indraprastha Institute of Information Technology Delhi India Punjab Engineering College (Deemed to be University) Chandigarh India Chitkara University Institute of Engineering & Technology Punjab India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Through the comparison of a normal Convolutional Neural Network (CNN) with a pre-trained DenseNet201 model, this study investigates the use of deep learning techniques for the diagnosis of lung cancer using histopathology pictures. Using 1190 CT scan pictures from 110 cases classified as normal, benign, or malignant, the research made use of the lung cancer dataset from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD). ROC curves for many classes, micro- and macro-average ROC regions, and confusion matrices were used to assess the models. DenseNet201 outperformed CNN in terms of accuracy (micro-average ROC area of 0.99 versus 0.70), with results showing a notable difference in performance between the two models. Gradient-weighted Class Activation Mapping (Grad-CAM), which provides visual explanations for the models' predictions, was put into use in the study to improve model interpretability. Grad-CAM representations demonstrated DenseNet201 ‘s improved discriminative capabilities by illuminating its more focused attention on disease-relevant features in the pictures. By demonstrating how transfer learning and explainable artificial intelligence (AI) techniques can improve lung cancer diagnosis, this research advances the field of medical image analysis. The study's conclusions point to the possibility of developing more precise and transparent diagnostic tools by fusing explainable AI techniques with sophisticated deep learning architectures, which could enhance oncology clinical decision-making. But in order to determine the model's generalizability and practical effectiveness, more testing on bigger, more varied datasets and in clinical settings is required, which validates the route that current methods will take in the future.

关键词： Deep learning Visualization Explainable AI Histopathology Decision making Lung cancer Transforms Predictive models Convolutional neural networks Medical diagnostic imaging

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Real-Time Sign Language to Speech Converter Using OpenCV and MediaPipe 5

Real-Time Sign Language to Speech Converter Using OpenCV and...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Pathak, Anvisha Patil, Niraj Padhy, Prashant Jadhav, Adarsh Rukhande, Smita Gadhikar, Lakshmi Fr. Conceicao Rodrigues Institute of Technology Dept. of Information Technology Navi Mumbai India Fr. Conceicao Rodrigues Institute of Technology Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798331522100

关键词： Gesture recognition

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Analysis and Classification of Heart Disease and COVID 19 Using Electrocardiogram and Deep Learning

Analysis and Classification of Heart Disease and COVID 19 Us...

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Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： Thoutireddy Shilpa Bura Vijay Kumar Nagendar Yamsani A. Harshavardhan Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak Telangana School of Computer Science and Artificial Intelligence SR University Warangal India Dept. of CSE (AIML&IoT) VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

The health industry has access to a significant amount of data. All this data is frequently overlooked and underutilized. Many diseases can be diagnosed, prevented, or cured by employing such a large amount of data. Diseases such as cardiovascular diseases, cancer, tumors, Alzheimer's disease, and heart diseases pose a significant threat to humanity, claiming an estimated 17.9 million lives each year. Since its identification in December 2019, COVID-19 has become a pandemic. COVID-19-related deaths are continuing to rise, and early detection is becoming increasingly important. Utilizing ECG copies and deep learning algorithms, an ideal way for automatically diagnosing Corona virus and irregular heartbeats is proposed. ECGs are segmented. Each segment of the ECG is an attribute, and the photos constitute a dataset. Following that, the resulting scanned copies of ECG are given as input into a Convolutional Neural Network (CNN). Using deep learning system and ECG data, it will be possible to identify cardiovascular changes automatically. This along with the appearance of cardiovascular changes due to abnormalities in the heart but also recognizes impact made by COVID-19. This also suggests that the DL model could be used to detect COVID-19 and heart diseases. The proposed study, we hope, might provide a significant decision-making framework for healthcare practitioners.

关键词： COVID-19 Heart Deep learning Industries Pandemics Medical services Electrocardiography

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