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A Mobile Application for the Crowdsensing of Road Pavements Conditions

A Mobile Application for the Crowdsensing of Road Pavements ...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Giovanni Delnevo Nicola Bartolucci Silvia Mirri Paola Salomoni Catia Prandi Dept. of Computer Science and Engineering University of Bologna Italy Bachelor Degree in Computer Science and Engineering University of Bologna Italy

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Urban environments face significant challenges due to deteriorating road pavements, affecting all transportation modes' safety and comfort. Traditional methods of road assessment are costly and infrequent, but advancements in mobile technology and crowdsensing offer real-time, large-scale data collection solutions. This paper introduces “ShakeSensing”, a mobile application that uses Android device sensors to measure vibrations experienced by cyclists and scooter users, providing insights into road quality. The collected data, analyzed to assess road conditions, can benefit both users and municipal authorities, promoting safer and more comfortable travel.

关键词： Vibrations Crowdsensing Roads Urban areas Transportation Vibration measurement Mobile applications Sensors Safety Sustainable development

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A Survey on Deepfake Video Detection Techniques Using Deep Learning 2

A Survey on Deepfake Video Detection Techniques Using Deep L...

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2nd International Conference on Next Generation Intelligent Systems, ICNGIS 2022

作者： Das, Athirasree Viji, K.S Angel Sebastian, Linda College of Engineering M-Tech Scholar Dept. of Computer Science and Engg Kerala Kidangoor India Assistant Professor College of Engineering Dept. of Computer Science and Engg Kerala Kidangoor India

ISBN: (纸本)9781665467926

Deep learning is an effective technology that has been widely used in different ways. 'DeepFake' videos are generated using deep learning technology called generative adversarial network where the videos are created with swaped faces in a video, altered facial expressions, change of gender, creation of fake video content and altered facial features. Fake videos are used in the situations like extortion of money, terrorism events or create political agitation. The deepfake technologies are used for positive purposes, such as film-making and virtual reality. The good quality results from deepfakes are very hard to recognised with people eyes. Many deep learning techniques are built to detect deepfake content in images and videos. A comparative study of the performance of various deepfake videos detection models in the deep learning is preseted in this paper. Convolutional Neural Network (CNN) models include ResNet, VGG16, Efficientnet etc.. Recurrent Neural Network (RNN) model includes Long Short-Term Memory LSTM. © 2022 IEEE.

关键词： Support vector machines

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Financial Viability of PV Grid for an Intelligent System of Residential Buildings in sub-urban Areas of Punjab, Pakistan 16

Financial Viability of PV Grid for an Intelligent System of ...

引用

16th Seminar on Power Electronics and Control, SEPOC 2024

作者： Shehzad, Moazzam Jamal, Nasir Muqeet, Hafiz Abdul Qureshi, Noman Sohaib Lincoln University College Dept. of Electrical Engineering Petaling Jaya Malaysia Punjab Tianjin University of Technology Dept. of Electrical Engineering Lahore Pakistan University of Engineering and Technology RCET Campus Dept. of Computer Science Lahore Pakistan

ISBN: (纸本)9798350379419

With fuel prices rising and environmental concerns intensifying, the financial burden on consumers and businesses has shown an upward trajectory, prompting a search for sustainable alternatives sources. This study investigates the financial feasibility of installing a grid-tied solar photovoltaic (PV) system in residential buildings in suburban areas of Punjab, Pakistan. The research uses a complete intelligent financial model to assess the feasibility of a grid-connected solar PV system, incorporating parameters such as solar irradiance levels, system size, capital costs, operating expenses, energy rates, its incentives, and payback periods in a systematic computational optimization methodology. The shift to solar photovoltaic systems provides an affordable way to lower energy bills that can also lessen the dependency on fossil fuels. Additionally, solar PV systems help significantly to reduce greenhouse gas emissions and environmental damages by utilizing the clean energy of the sun. This is in line with worldwide efforts to battle climate change and protect the planet from environmental concerns. This research uses RET Screen software to evaluate the financial feasibility with GHG reduction analysis as well. The study's findings are useful for homeowners, policymakers, and stakeholders interested in encouraging renewable energy adoption and sustainable development in Punjab, Pakistan's suburban region. © 2024 IEEE.

关键词： Computational Optimization Feed-In-Tariff Net Energy Metering Net Present Value Net-Metering

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Traffic Light Detection for Self-Driving Cars using the YOLOv8 architecture 24

Traffic Light Detection for Self-Driving Cars using the YOLO...

引用

2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Gupta, Aditya Gupta, Ayushi Raj, Gaurav Choudhury, Tanupriya Sar, Ayan Kotecha, Ketan Ozseven, Turgut Dept. of Computer Science and Engineering Sharda University Greater Noida India Uttarakhand Dehradun248007 India CSE Dept. Graphic Era Deemed to be University Uttarakhand Dehradun248002 India Maharashtra Pune India Department of Computer Engineering Tokat Gaziosmanpasa University Tokat Turkey

ISBN: (纸本)9798400716928

A traffic light detection dataset is used to evaluate the performance of the latest version of YOLOv8. Using this dataset and an ideal you only look at once (yolo), we can create a data-driven traffic light detection system that has both high detection accuracy and high performance during the training and detection process. The number of accidents caused by failure to obey traffic rules and traffic lights has increased dramatically. Self-driving cars are the solution to 90% of all traffic accidents, but the software must be efficient, fast, and reliable to accurately detect traffic signs. YOLO is the most advanced object recognition technology. © 2024 ACM.

关键词： computer vision

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Image-Based Attendance System using Facial Recognition 11

Image-Based Attendance System using Facial Recognition

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chauhan, Vineet Kumar Singh, Trilok Dixit, Abhishek Singh, Raushan Kumar Singh, Pradeep Kumar Singh, Juginder Pal Tmu University Dept. of Computer Science and Engg Moradabad India Invertis University Dept. of Computer Science and Engg. Bareilly India Vardhaman College of Engineering Dept. of Information Technology Hyderabad India GLA University Dept. of Computer Engg. and Application Mathura India

ISBN: (纸本)9781665487320

In today's digital age, attendance systems utilizing facial recognition are essential in schools, universities, companies, etc. One feature of the human body that might help identify a person is the face. Using a camera, we initially built a database of various people's faces for this project, and the recognizer algorithm would use this information. The system will be prepared to take attendance on its own after the database has been established. A cross-check will be performed between the recently acquired face image and the database. The attendance will be recorded and the data immediately saved into the excel sheet if the same face's image is found in the database. This attendance-based system using Face Recognition will be developed using PCA (Principal Component Analysis) and CNN (Convolutional Neural Network). © 2022 IEEE.

关键词： Database systems

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Impact of Preprocessing Thermographs on Effective Classification of Potatoes 4

Impact of Preprocessing Thermographs on Effective Classifica...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Nandhitha, N.M. Emalda Roslin, S. Shivasreetha, S. Muthiah, M.A. Rajasekar, B. Sathyabama Institute of Science and Technology Department of ECE Chennai600119 India St.Joseph's College of Engineering Computer Science and Engineering Chennai600119 India Sathyabama Institute of Science and Technology Dept. of ECE Chennai600119 India

ISBN: (纸本)9798331515720

InfraRed Thermography can be used for assessing the quality of potatoes. Thermographs acquired using IR camera is processed for identifying the presence/absence of diseases in potatoes. Raw pseudocolor images as acquired from thermographs for four different classes (holes, fungus, badly affected potatoes and normal potatoes) are fed as input to ALEXNET and the performance was studied. Performance metrics cannot be improved as the background intensity and the Region of Interest (RoI) intensity are nearly equal (low contrast images). In this paper, prior to feeding thermographs as inputs to ALEXNET, thermographs were enhanced and the performance was studied. Secondly, only potato region was identified and the background intensities are made as lowest (zero) to improve the contrast. These images are then fed to ALEXNET and the performance is studied. Also, in order to reduce the computational complexity of ALEXNET, the convolution layer and ReLu layer above the fully connected network are removed and a modified ALEXNET architecture is obtained. After training, modified ALEXNET is then used for the above three sets of thermographs and the performance is studied. It is found that modified ALEXNET not only reduces the computational complexity but also has an accuracy of 75% for RoI separated thermographs much greater than that of ALEXNET and that of enhanced thermographs. © 2024 IEEE.

关键词： Image segmentation

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Exploring Climate Change Prediction Dynamics Using Machine Learning Approach: A Review

Exploring Climate Change Prediction Dynamics Using Machine L...

引用

Computing, sciences and Communications (ICCSC), International Conference on

作者： Shashank Tiwari Shikha Verma Rosey Chauhan Neha Gupta Dept. of Computer Science Sharda Unviersity Gerater Noida India Dept. of Computer Science ABES Engineering College Ghaziabad UP India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Climate refers to the long-term weather patterns of a region. Variations in the weather have a lot of implications on our daily lives. Explore the application of deep learning techniques in climate prediction and related fields. It focuses on Long Short-Term Memory (LSTM) models for forecasting air quality, greenhouse temperature control, and urban flood water accumulation using Gradient Boosted Decision Trees (GBDT). The analysis concludes that deep learning, particularly LSTM, offers promising accuracy for various climate-related predictions. Challenges such as data scarcity, non-stationarity, and high dimensionality are addressed, highlighting the need for advanced models to capture complex climate system dynamics and human influences.

关键词： Deep learning System dynamics Reviews Atmospheric modeling Predictive models Temperature control Decision trees Forecasting Long short term memory Meteorology

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Machine Learning Approaches to Predict Post Covid-19 Severity: A Detail Case Study

Machine Learning Approaches to Predict Post Covid-19 Severit...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Mathur, Richa Dutta, Ritam Bansal, Payal Kumar, Rajeev Poornima University Dept. of Computer Scinece & Engineering Rajasthan Jaipur303905 India Rajasthan Jaipur302022 India Poornima Institute of Engineering & Technology Dept. of Electronics & Comm. Engineering Rajasthan Jaipur302022 India Moradabad Institute of Technology Dept. of Computer Science & Engineering Uttar Pradesh Moradabad244001 India

ISBN: (纸本)9798350378047

The entire world has witnessed a horrific pandemic in last three years, whose long term and short-term impact to human body has been observed in large scale in these post covid-19 years. Machine learning (ML) techniques have been successfully implemented in medical field since last one decade. The emergence of post covid-19 pandemic symptoms are compelling researchers to re-search for minute detection and predict patient's probable infection rate. Long lasting chest pain, breathing issues, liver and kidney dialysis issues have been apparently visible as post covid-19 symptoms. In this paper, the journals regarding the prediction of covid-19 severity based on symptoms have been discussed. Several researchers have established deep learning-based models for the prediction. They have used the test data from the hospitals for their research process. The KNN models, ANN models, and SVM models have been discussed. The limitations of the past research process have been evaluated to be the biased test data set and missing values. The methodology that was used for the research process has been described. Mixed method of data collection was used for the research processes. The secondary data was collected from reliable sources for this research process. Considerable amount of test data has been retrieved from Kaggle database regarding post covid-19 patient's symptoms. The data analysis is performed on the Weka tool using various machine learning models. © 2024 IEEE.

关键词： COVID-19

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Discovering Efficient Fused Layer Configurations for Executing Multi-Workloads on Multi-Core NPUs

Discovering Efficient Fused Layer Configurations for Executi...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Younghyun Lee Hyejun Kim Yongseung Yu Myeongjin Cho Jiwon Seo Yongjun Park Dept. of Artificial Intelligence Hanyang University Dept. of Computer Science and Engineering Yeonsei University Dept. of Computer Science Hanyang University SAPEON Korea Inc

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

As the AI industry grows rapidly, Neural Processing Units (NPUs) have been developed to deliver AI services more efficiently. One of the most important challenges for NPUs is task scheduling to minimize off-chip memory accesses, which may occur significant performance overhead. To reduce memory accesses, multiple convolution layers can be fused into a fused layer group, which offers numerous optimization opportunities. However, in most Convolutional Neural Networks (CNNs), when multiple layers are fused, the on-chip memory utilization of the fused layers gradually decreases, resulting in non-flat memory usage. In this paper, we propose a scheduling search algorithm to optimize the fusion of multiple convolution layers while reducing the peak on-chip memory usage. The proposed algorithm aims to find a schedule that simultaneously optimizes execution time and peak on-chip memory usage, despite a slight increase in off-chip memory accesses. It organizes the search space into a graph of possible partial schedules and then finds the optimal path. As a result of the improved on-chip memory usage, multiple workloads can be executed on multi-core NPUs with increased throughput. Experimental results show that the fusion schedule explored by the proposed method reduced on-chip memory usage by 39%, while increasing latency by 13%. When the freed on-chip memory was allocated to other workloads and the two workloads were executed concurrently in a multi-core NPU, a 32% performance improvement could be achieved.

关键词： Industries Schedules Job shop scheduling Convolution Processor scheduling Throughput System-on-chip

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Enhancing the Prediction of Growth of Footballers using Real-Life Statistics and Machine Learning 1

Enhancing the Prediction of Growth of Footballers using Real...

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Parida, Subham Thilak, K. Deepa Singh, Ransher Srm Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Dept. of Networking and Communication Chennai India

ISBN: (纸本)9781665497107

Futbol Club Barcelona, more popularly known as FC Barcelona is one of the most successful clubs in the history of football winning the UEFA Champions' league, Europe's premier club competition as well as the biggest club competition in world football, five times and having one of the highest tallies of trophies in the world. The club has also had the highest revenue of all in the world for a few years and is as of now, the most valuable club in the world. But the club is in a debt of 1.6 billion as of 2021. The question naturally arises, what did a club like Barcelona do wrong to meet such fate despite being so commercially successful? Though the pandemic has had a sizable impact on the club's financial wellbeing, the bulk of the blame must go to the horrible transfer record that Barcelona has had since their last UEFA Champions' league triumph in 2015. FC Barcelona has spent a whopping 1.3 billion on transfers alone since June 2015. The signings were supposed to prolong the club's stay at the summit of club football, ironically these have brought the club down significantly. Players like Phillipe Coutinho, Ousmane Dembele and Antoine Griezmann who were bought in for a combined fee of 500 million, were supposed to be some of the best in the world, but didn't have remotely good spells at Barcelona. They certainly were world class talents, so why did they barely get into the starting 11 when they should have been the next generation of superstars? Was it pressure, injuries or were they just not the right fit for Barcelona? Though Barcelona is an extreme example, every year dozens of big money transfers fail despite some of the most experienced and smartest brains working on them. Though player potential is immensely important for a player to achieve success, in the end it invariably depends on the playing style of the player and the team. The primary objective of this paper is to leverage analytics to identify players who are the best fit for the teams. The idea is to

关键词： Machine learning

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