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A vision-based litter detection and classification using SSD MobileNetv2 10

A vision-based litter detection and classification using SSD...

引用

10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Balmik, Archana Barik, Subhasish Jha, Mrityunjay Nandy, Anup National Institute of Technology Rourkela Department of Computer Science & Engineering Rourkela India

ISBN: (纸本)9781665490993

Municipal solid litter generation has steadily increased significantly in recent years. To prevent waste from collecting in the environment, it is beneficial to clean up wastes and rubbish using autonomous cleaning equipment such as unmanned surface vehicles. Cleaning efficiency requires a high-accuracy and reliable object detection system. Many machine learning techniques are explored so far to produce litter recognition systems. There are few studies on the effectiveness of cuttingedge deep learning object detection algorithms in the domain of litter localization and detection. We propose a detection and classification module for five different waste objects in this research. The dataset is created by gathering photos from various sources and under various environmental conditions at our institute. In order to enhance the quantity of data, data augmentation techniques are used. Image processing and feature extraction steps are applied to enhance the performance of the proposed model. The SSD MobileNetv2 model is employed for litter recognition which detects and identifies distinct contaminants and hazardous waste materials with a mean Average Precision (mAP) of 0.84. The proposed module aids in environmental cleanup by efficiently detecting the waste objects present in the surrounding environment. © 2023 IEEE.

关键词： Object detection

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TruceNet: A CNN-Based Model for Accurate Classification of DeepFake Images

TruceNet: A CNN-Based Model for Accurate Classification of D...

引用

2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Rao, Darshan Utturwar, Kaustubh Shelke, Tejas Patil, Anuj Sarda, Ekta Ramrao Adik Institute of Technology Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350301595

With the rise of GANs, the prevalence of Deepfakes has increased, resulting in significant privacy, security, and social concerns. To tackle this issue, we propose TruceNet, a novel approach that utilizes a CNN architecture to detect Deepfakes in images. Our method analyzes various image features to determine if manipulation has occurred. Through experiments on a comprehensive dataset of Deepfakes and non-Deepfakes, we evaluate TruceNet's effectiveness. Our CNN architecture achieves high detection accuracy, outperforming state-of-the-art methods. TruceNet exhibits robustness against common Deepfake manipulations such as face swapping, expression modification, and Neural Texture Synthesis. It consistently achieves an average accuracy of 94.2% in correctly classifying manipulated images, maintaining a precision of 95% across different manipulation scenarios. The research findings highlight TruceNet's significant contribution in mitigating Deepfake risks and enhancing digital media security and trustworthiness. By accurately detecting Deepfakes, TruceNet offers a reliable solution to combat the growing threat posed by manipulated images. © 2023 IEEE.

关键词： Digital storage

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Tampering Detection and Localization on Copy-Move Images Using Deep Learning Approaches

Tampering Detection and Localization on Copy-Move Images Usi...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Rao, Kalabarige Lakshmana Tejaswari, Sanapathi Meghana, Patnana Juveria, Shaik Sathwika, Sankili Gmr Institute of Technology Rajam Department of Computer Science & Engineering Vizianagaram India

ISBN: (纸本)9798350300857

Images can be used as legal evidence in forensics, journalism, and other fields. Image tampering is modifying images using modern technologies, which might create false evidence. There are many tampering with images like copy-move image forging, image slicing, and recoloring. Now the detection of those types of images whether tampered with or not and the process of recognizing copied and pasted portions for the copy-move tampered images is a challenging task. In this connection, this work employed CNN, ResNet-50, and VGG-16 for the detection of tampering images. These models were trained on CASIAv2 and COMOFOD datasets. Moreover, error-level analysis techniques and Resizing are used to preprocess for detection. SIFT, and Gray Scaling were applied to preprocess the Copy-Move images to identify similar features in the image. Furthermore, preprocessed images are fed to the DBSCAN algorithm to locate the portions of the image where it is copied and pasted in copy-move tampered images. From the results, it was observed that the CNN model trained on the CASIAv2 dataset outperformed all other models. Hence, it was tested on the MICC-F220 dataset followed by DBSCAN for localization of tampered images. © 2023 IEEE.

关键词： Convolutional neural networks

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Traffic Sign Detection and Recognition Using Dense Connections in YOLOv4 7th

Traffic Sign Detection and Recognition Using Dense Connectio...

引用

7th International Conference on computer Vision and Image Processing, CVIP 2022

作者： Saxena, Swastik Dey, Somnath Department of Computer Science and Engineering Indian Institute of Technology Indore Indore India

ISBN: (纸本)9783031314162

A self-driving car is a growing technology in India where detection of traffic sign in an unconstrained environment is a challenging task due to its small size. With the development of deep neural networks, many models for object detection have been developed. In this work, we have used a single-stage detection model YoloV4 with further improvements in detection neck for detection of traffic signs. We have used dense connections in place of normal connections of the model for better feature propagation. This improves the accuracy with less inference time. We have conducted our experiments on bench-marked Chinese traffic sign dataset, Tshigua-Tenscent 100K dataset (TT-100K). We have achieved accuracy of 94.30% with 32 FPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Traffic signs

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Play Store App Rating Prediction Using Machine Learning & Deep Learning Regression Techniques 2

Play Store App Rating Prediction Using Machine Learning & De...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Biswal, Bhramara Bar Das, Satya Narayan Sahu, Satyanarayan Banerjee, Shobhan Computer Science and Engineering Gandhi Institute of Engineering and Technology Gunupur India Department of Engineering Technology Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798350308846

The rating & review of an app in the App Store or Play Store plays an essential role for end users to get information about the app based on other people's experiences with that app. The reviews might be context-based, but the ratings give a firsthand impression to the users, simply looking at scores out of five. In this paper, we have performed multivariate analysis on the Google Play Analysis Dataset to determine the critical features and prune off the redundant ones, followed by which we compared the accuracy of various regression models in predicting the ratings of various apps in the dataset. © 2023 IEEE.

关键词： Artificial Neural Network Feature Importance Multivariate Analysis Rating prediction Regression

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Makaton Sign Language Recognition

Makaton Sign Language Recognition

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Prince, Nikhil R Thayil, Rohan Ousephachen Anto Praveena, M.D. Prince Mary, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350333244

Makaton is a widely used sign language system that is primarily used by individuals with communication difficulties, such as those with Autism, Down syndrome, and other developmental disorders. However, despite its widespread usage, there is a lack of technology for accurately recognizing and understanding Makaton signs. This paper proposes a model that combines MediaPipe Holistic with a neural network, such as SimpleRNN, LSTM, or GRU, to recognize Makaton sign language (MSL). The model is trained on a custom dataset of Makaton sign language videos using backpropagation and the Adam optimizer. The proposed network achieved a high accuracy on the test set, demonstrating its effectiveness in recognizing Makaton sign language. This study introduces an innovative method for advancing the development of systems that recognize sign language by utilizing MediaPipe Holistic in conjunction with a neural network. This can improve the way technology interacts with people who have hearing impairments. © 2023 IEEE.

关键词： Long short-term memory

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Real - Time AI and Machine Learning Applications in Healthcare Management 1

Real - Time AI and Machine Learning Applications in Healthca...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Poornima, D. Premalatha, J. Sarojini Abirami, G. Triveni, K. Bobby, M.P. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Real-time Artificial Intelligence (AI) and Machine Learning (ML) programs have emerged as a number one motive force for considerably altering patient care and management inside the healthcare control area. Staff shortages and affected person document management are among the fundamental problems dealing with the healthcare industry. In an effort to fight those issues, actual-time AI and ML technology offer a demonstration of optimism with the aid of facilitating quick decisions and higher affected person care. Having those technologies work together ensures that time-sensitive healthcare statistics is processed quick, which improves both useful resource allocation and service shipping. However, there are challenges related to the implementation of real-time AI and ML packages in healthcare management. Interoperability, the mixing of disparate records sources, and privateness and security concerns all pose great difficulties. In addition, an intensive and powerful approach is needed to assure the processing of healthcare facts in real-time. To better manipulate patient facts inside electronic health information, an IoT-based totally Artificial Deep Neural Network (IoT-ADNN) is offered in this studies. There are a huge style of revolutionary uses for actual-time (AI-ML) in healthcare administration. These gear provide healthcare carriers with actual-time information, which in flip results in better resource allocation and a more gratifying affected person revel in. In addition, they enhance information security and accessibility, making sure privateness and ease of get entry to to touchy healthcare facts. Data transmission and shipping quotes, postpone estimation accuracy, energy performance, and throughput have all been dramatically improved. The importance and usefulness of actual-time AI and ML systems in healthcare management are emphasised by means of these measurements. © 2023 IEEE.

关键词： Deep neural networks

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Blockchain and Reverse Auction-based EVs Energy Trading Approach for Optimal Pricing 13

Blockchain and Reverse Auction-based EVs Energy Trading Appr...

引用

13th International Conference on Cloud Computing, Data Science and engineering, Confluence 2023

作者： Kakkar, Riya Gupta, Rajesh Jadav, Nilesh Kumar Tanwar, Sudeep Agrawal, Smita Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India

ISBN: (纸本)9781665462631

This paper proposes a reverse auction and blockchain-based electric vehicles (EVs) energy trading (ET) approach by adopting the smart grid. EVs can be classified into prosumer or consumer during the ET in the proposed approach. We further introduced the Interplanetary File System (IPFS)-based cost-efficient ET approach considering the sixth-generation (6G) wireless network to address the scalability and response time issues of the data transactions. Furthermore, the proposed approach leverages a reverse auction mechanism for optimal ET for EVs. The reverse auction mechanism maximizes the profit for EVs participating in the ET scheme using smart grid. Based on the reverse auction mechanism, a consumer can choose to trade energy with the prosumer or smart grid based on solar panel efficiency. Finally, the proposed approach is simulated considering the various performance metrics such as transaction efficiency, profit for consumers, and convergence to enable an optimal and efficient ET system. © 2023 IEEE.

关键词： Blockchain

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Park with Ease: Evaluating Usability to Design Intuitive Interfaces for Car Parking Applications 9

Park with Ease: Evaluating Usability to Design Intuitive Int...

引用

9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Ashraf, Anika Sultana, Zinia Islam, Iyolita Pinky, Amrin Akhter Toma, Sabrina Afrin Zinia, Afrina Islam, Muhammad Nazrul Military Institute of Science and Technology Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798350319651

With a smartphone app or web interface, users with different demographic profiles may book and pay for parking spaces, cutting down on the time and stress involved in locating a place. Hence, such parking systems should be usable and intuitive to end users. Therefore, this research aims to evaluate the usability of existing car parking applications and to propose an intuitive and usable car parking application. To attain these objectives, three smart car parking mobile applications (Parking Koi, ParkWhiz, and SpotHero) were selected and evaluated through heuristic evaluation and a user study. The studies revealed several usability problems such as lack of feedback, instructions, flexibility and adaptability. According to the severity of issues, the Park Whiz application encountered the least number of issues in total, while the SpotHero program experienced the highest number of problems. Comparing with Parking Koi and SpotHero, Parkwhiz's average level of severity is substantially higher. The user study revealed that the three car parking applications' interfaces are poorly designed and provided the suggestions for improvement to enhance user attention. Finally, a prototype of a car parking application with an intuitive UIs is proposed to address the revealed usability problems. © 2023 IEEE.

关键词： User interfaces

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Investigation of Feature Extraction methods for Classifying Brain Abnormalities in Ultrasound Brain Images using Signal Processing Network 1

Investigation of Feature Extraction methods for Classifying ...

引用

1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Jabez, J. Franklin, Ramya G Ronald Doni, A. Kamalesh, Murari Devakannan Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

A key a part of early analysis and treatment of neurological illnesses is the detection of anomalies inside the mind through ultrasound scans. Using a Signal Processing Network (SPN), this studies seems into the cost of characteristic extraction techniques for identifying and categorizing brain problems in ultrasound mind pics. Timely scientific action is appreciably aided through the early and accurate analysis of mind abnormalities in ultrasound pics. In mild of these findings, it's far obvious that accurate categorization techniques are vital for improving patient consequences through facilitating in advance diagnoses. It is hard to stumble on abnormalities in the brain using ultrasound pics because of historical past noise, artifacts, and the need for correct localization. This paper attracts attention to these problems and proposes a strategy for overcoming them through a Signal Processing Network. For better class of brain problems, this studies proposes a unique method that combines function extraction techniques and a Signal Processing Network: Deep learning-based improved function Ultrasound Brain Images (DL-EFUBI). The technique employs state-of-the-art sign processing algorithms to extract distinguishing traits from ultrasound photos, taking into consideration more particular class of abnormalities. The proposed technique shows promise in some of therapeutic programs, such as the analysis of brain tumors, cysts, and other irregularities at an early degree. It can assist scientific practitioners make higher choices, which in flip blessings affected person care and results. Extensive simulation studies, wherein the cautioned method is compared to mounted methods, is used to assess the approach's efficacy. The effects display that the method has the ability to outperform general approaches close to type accuracy, sensitivity, and specificity. © 2023 IEEE.

关键词： Feature extraction

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