2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)最新文献_第6页

Song Playlist Generator System Based on Facial Expression and Song Mood 基于面部表情和歌曲情绪的歌曲播放列表生成系统

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670976

Kevin Patel, R. K. Gupta

{"title":"Song Playlist Generator System Based on Facial Expression and Song Mood","authors":"Kevin Patel, R. K. Gupta","doi":"10.1109/aimv53313.2021.9670976","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670976","url":null,"abstract":"Because of the hectic pace that people have nowadays, life is incredibly hectic. People are increasingly inclined to listen to music while performing their daily duties which help them relax after a stressful day. As a result, songs become important part of daily lifestyle. Due to the huge demand several music players have entered to the market and try to attempt to deliver the best possible music recommendation for the customer. This paper proposes a Deep Learning based approach for the playlist generation based on human current mood with the help of user’s past history of song selection. In this approach we are trying to generate playlist from the emotion of the user to add touch of current situation of user mood and user personal choices of the songs for providing more personalized experience. After introduction of the Convolutional Neural Network object detection, Image classification, Emotion detection tasks reaches great height. In the proposed method, we use convolution neural network (CNN) for emotion detection task and artificial neural network (ANN) for the song classification task. Experiment result says that our suggested model achieve 84% accuracy on FER-13 dataset which contain around 14k facial images. For song classification task we have used different song-features which is extracted from Spotify music player. We have achieved 82% accuracy in song classification task. Currently this system is only with Spotify music player. Motivation of this approach is to provide better song recommended playlist based on user current mood.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132647807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Plant Disease Prediction and classification using Deep Learning ConvNets 基于深度学习卷积神经网络的植物病害预测与分类

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670918

A. Lakshmanarao, M. Babu, T. Kiran

{"title":"Plant Disease Prediction and classification using Deep Learning ConvNets","authors":"A. Lakshmanarao, M. Babu, T. Kiran","doi":"10.1109/aimv53313.2021.9670918","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670918","url":null,"abstract":"A country's inventive growth is dependent on the agricultural sector. Agriculture, the foundation of all nations, offers food and raw resources. Agriculture is hugely important to humans as a food source. As a result, plant diseases detection has become a major concern. Traditional methods for identifying plant disease are available. However, agriculture professionals or plant pathologists have traditionally employed empty eye inspection to detect leaf disease. This approach of detecting plant leaf disease traditionally can be subjective, time-consuming, as well as expensive, and requires a lot of people and a lot of information about plant diseases. It is also possible to detect plant leaf diseases using an experimentally evaluated software solution. Currently, machine learning and deep learning are using in recent years. The agriculture sector is also not a exception for machine learning. In this paper, we applied \"Convnets\" for plant disease detection and classification. We collected a PlantViallge dataset from Kaggle. It contains images of 15 different classes of plant leaves of three different plants potato, pepper, tomato. We divided the dataset into three datasets and applied Convnets on three datasets. We achieved an accuracy of 98.3%,98.5%,95% for potato plant disease detection, pepper plant disease detection, tomato plant disease detection. Experimental results have shown that our model achieved a good accuracy rate for plant leaf disease detection and classification.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133894314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Improving Machine Learning based Groundwater Level Estimation using Geological Features 利用地质特征改进机器学习的地下水位估算

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9671011

A. Lad, Khushali Patel, Soumya Soumya, Yash Solanki

{"title":"Improving Machine Learning based Groundwater Level Estimation using Geological Features","authors":"A. Lad, Khushali Patel, Soumya Soumya, Yash Solanki","doi":"10.1109/aimv53313.2021.9671011","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9671011","url":null,"abstract":"Estimation of Groundwater level is crucial for managing water resources. Forecasting groundwater level changes can help determine the efficient utilisation of groundwater resources and drive water conservation efforts, especially in arid regions. Existing works have used machine learning techniques to estimate groundwater levels using meteorological data. However, they have restricted the scope of their research to areas with abundant, continuous time-series data. In this paper, we aim to address the issue of sparse data in estimating groundwater levels. This study explores a data-driven approach and thus does not introduce a new machine learning model. We expand the input parameters to incorporate geological and demographic data along with traditional meteorological data. We have collected data of the Kutch region in Gujarat, spanning 11 years with varying data availability at monitoring sites. Using techniques like Random Forest Regression and Neural Networks, we can improve the estimation of groundwater levels compared to using traditional features. We also analyse causal effects of different values of Geological parameters by extending the concept of treatment effect and provide interpretability of the estimation models. The results presented here indicate that factors like soil type and depth are essential in estimating groundwater level and can improve performance on sparse time-series data. The treatment effect analysis also provides results that conform to existing knowledge, thereby bridging the semantic gap between computer science and hydrogeology domains.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"479 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133046269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

YouTube Content Analysis For Hair Extension’s Business YouTube内容分析头发延伸的业务

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/AIMV53313.2021.9670965

M. Mishra, Shreyasi Kendurkar, Aditi Gupta, Astha Kawatra, Chaitali Chandankhede

{"title":"YouTube Content Analysis For Hair Extension’s Business","authors":"M. Mishra, Shreyasi Kendurkar, Aditi Gupta, Astha Kawatra, Chaitali Chandankhede","doi":"10.1109/AIMV53313.2021.9670965","DOIUrl":"https://doi.org/10.1109/AIMV53313.2021.9670965","url":null,"abstract":"From2018 to 2024, the global hair extension market is projected to grow by 8%, reaching more than $5 billion. Hair care was the second-largest segment in the global beauty industry in 2017, accounting for 18% of total sales, after skincare. A big driver of this market is the need to enhance one's physical appearance which most of the times are affected by any hair loss diseases like cancer, alopecia, pregnancy, thyroid, and so on. Endorsing wigs and hair extensions is becoming increasingly common on social media. Specialty shops, hair salons and beauty stores, and hair clinics account for a large portion of the sale of hair wigs and extensions.Our System provides a unique analytical view for solving problems of customers by helping them choose amongst various brands of hair extensions. It does this by using the video’s content to perform sentiment analysis rather than just focusing on public comments on the videos. It also performs statistical analysis and provides an overview about the best channels and influencers. To give a better decision base to customers, the paper focuses on providing content extraction of various YouTube videos covering fashion influencers' reviews as well as common people’s reviews and for the investors, insights of market trends to judge its potential.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122220144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A CNN-GRU-SVR based Deep Hybrid Model for Water Quality Forecasting of the River Ganga 基于CNN-GRU-SVR的恒河水质深度混合预测模型

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670916

Aishwarya Premlal Kogekar, Rashmiranjan Nayak, U. C. Pati

{"title":"A CNN-GRU-SVR based Deep Hybrid Model for Water Quality Forecasting of the River Ganga","authors":"Aishwarya Premlal Kogekar, Rashmiranjan Nayak, U. C. Pati","doi":"10.1109/aimv53313.2021.9670916","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670916","url":null,"abstract":"Water pollution is a global problem. In developing countries like India, water pollution is growing exponentially due to faster unsustainable industrial developments. Recently, the river Ganga has been polluted faster and caused lots of diseases among humans and aqua-animals. Hence, continuous water quality monitoring with appropriate water quality management plans is required to maintain sustainable growth. The manual methods of water quality analysis are not suitable in order to get the proper results due to the involvement of life risk and high time consumption. Therefore, it is essential to move towards some advanced data collection, processing, and monitoring approaches that are easy, less costly, and fast. This can be achieved by using data-driven approaches like deep learning techniques due to their strong decision-making ability and automatically learning capabilities from their experience. Hence, a deep hybrid model using Convolutional Neural Networks - Gated Recurrent Units - Support Vector Regression (CNN-GRU-SVR) is proposed to forecast the water quality of the river Ganga using historical data. Here, only two crucial available water pollutants, such as dissolved oxygen and biochemical oxygen demand, collected from Uttar Pradesh Pollution Control Board’s official website, are considered for forecasting. The effectiveness of the proposed model is experimentally established by comparing the results with that of the five different deep learning models that have been developed as baseline models.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123896531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Prognosis of Sleep Stage Classification Using Machine Learning Techniques Applied on Single-channel of EEG signal of both Healthy Subjects and Mild Sleep effected Subjects 应用机器学习技术对健康受试者和轻度睡眠障碍受试者单通道脑电信号的睡眠阶段分类预测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670967

S. Satapathy, Hari Kishan Kondaveeti

{"title":"Prognosis of Sleep Stage Classification Using Machine Learning Techniques Applied on Single-channel of EEG signal of both Healthy Subjects and Mild Sleep effected Subjects","authors":"S. Satapathy, Hari Kishan Kondaveeti","doi":"10.1109/aimv53313.2021.9670967","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670967","url":null,"abstract":"Sleep is a basic requirement of human life. It is one of the vital roles in to the human life to maintain the proper mental health, physical health and quality of life. In this proposed research work, we conduct an automated sleep stage classification to proper investigation of irregularities occurred during sleep based on single channel of electroencephalogram (EEG) signal (SleepEEG) with using of machine learning approaches. The major advantage of this proposed research work over standard polysomnography method are: 1) it measures the sleep irregularities during sleep by considering two different medical condition subjects of different gender with different age groups.2) One more important objective of this proposed sleep study is that here we obtain different session recordings to investigate on sleep abnormality patterns, which can help to find better diagnosis towards treatment of sleep related disorder.3)In present work, we have obtained 15s time-framework epochs from individual subjects to check which window size is more effective towards identification on sleep irregularities.The present research work based on two-state sleep stage classification problem based on single channel of EEG signal were performed in different stepwise manner such as acquisition of data from participated subjects, preprocessing, feature extraction,feature selection and classification. We obtained the EEG data from ISRUC-Sleep data repository for measuring the performances of the proposed framework, where the sleep stages are visually labelled. The obtained results demonstrated that the proposed methodologies achieves high classification accuracy, which support to sleep experts for accurately measure the irregularities occurred during sleep and also helps the clinicians to evaluate the presence and criticality of sleep related disorders.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123954956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Computer Vision enabled Adaptive Speed Limit Control for Vehicle Safety 计算机视觉使自适应限速控制车辆安全

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670944

A. Lad, Prithviraj Kanaujia, Soumya, Yash Solanki

{"title":"Computer Vision enabled Adaptive Speed Limit Control for Vehicle Safety","authors":"A. Lad, Prithviraj Kanaujia, Soumya, Yash Solanki","doi":"10.1109/aimv53313.2021.9670944","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670944","url":null,"abstract":"Over speeding, especially by heavy vehicles, is the primary cause of accidents in India. The reason can be attributed to the lack of a framework to maintain strict road safety rules. This leads to heavy vehicles occupying a high-speed lane, which leads to frustration and rapid lane switching among passenger vehicles. With the recent advancements in Computer Vision and IoT, it is possible to enforce such safety rules without on-ground personnel. In this paper, we have proposed an IoT-based solution for vehicle speed control that uses computer vision to detect the lane and dynamically limit the vehicle's speed, thus discouraging higher speeds of certain vehicles on specific lanes. We have manually labelled ~1.2 lakh images of the TuSimple lane dataset for training the models. We have provided CNN models as a baseline and used a pixel counting based SVM method for detecting lanes which achieved CNN levels of accuracy while being computationally efficient. Our proposed solution aims to automate the regulation of speed on a per vehicle basis, which can be very effective in reducing the number of accidents in India.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123609495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Face Mask Detection Using Optimized CNN 基于优化CNN的口罩检测

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670939

Deepali J. Joshi, Adarsh Sharma, Shantanu Pingale, Chanchal Mal, Sangeeta Malviya, N. Patil

{"title":"Face Mask Detection Using Optimized CNN","authors":"Deepali J. Joshi, Adarsh Sharma, Shantanu Pingale, Chanchal Mal, Sangeeta Malviya, N. Patil","doi":"10.1109/aimv53313.2021.9670939","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670939","url":null,"abstract":"COVID-19 has had a rapid impact on people's lives, affecting global trade and transportation. Protecting against COVID-19 by wearing a face mask has become the new normal. Many public service providers will need clients to wear masks to access their services in the near future. As a result, in today's culture, face mask detection is essential. This study proposes attaining the aim by utilizing some basic platforms such as Machine Learning packages such as TensorFlow, Keras, and OpenCV libraries. The goal of this project is to reliably detect the face in an image and then determine whether or not the individual is wearing a mask. In addition, the model can detect the existence of a mask in real time. The mask detection dataset was compiled using Internet resources, and a Google form was constructed to collect photographs with and without masks. We examine optimum parameter values for the Sequential Convolutional Neural Network model in order to correctly detect the presence of masks without causing over-fitting. On camera or in real time, we want to see if a person wearing a face mask is actually wearing one.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"185 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122888594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient sea water Purification using Hybrid Nanofiltration system and ML for Optimization 混合纳滤系统和ML优化的高效海水净化

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670922

Vishwa P. Parmar, Akshit J. Dhruv

{"title":"Efficient sea water Purification using Hybrid Nanofiltration system and ML for Optimization","authors":"Vishwa P. Parmar, Akshit J. Dhruv","doi":"10.1109/aimv53313.2021.9670922","DOIUrl":"https://doi.org/10.1109/aimv53313.2021.9670922","url":null,"abstract":"The Earth has an abundance of water, about 70 percent of the globe is covered with water, wherein only 2.5 percent of freshwater is available for human usage. Due to the major issue of over-population and lack of pure water bodies, the problem of pure water scarcity has reached it’s peak. Hence, there is a demand of a system wherein efficiently pure water can be processed and there is a smooth flow of pure water. We have proposed a model which is cost-effective, environmental friendly, and responsive to the limitations of existing desalination and filtration plants making it an absolute system. The proposed model is 3 layer hybrid system, which is interconnected and is sequential. The system is a combination of sedimentation, amyloid carbon hybrid membranes and graphene oxide technology for complete purification of seawater. This paper presents a comparison between the existing techniques with our proposed model resolving better aspects. Additionally, the paper consists of the laboratory tested results of seawater, groundwater and tap water and by the analysis of that result we have shown the amount of purification required for seawater. As membranes are very sensitive and it is needed to change with time, we have proposed the machine learning approach which will look after the saline water which is coming inside the system and will keep track on water quality of incoming water. Also, we will use supervised algorithms and computer vision which will keep watch on membranes and will give alert when there is need to clean the membrane which will reduce the chance of changing them frequently. And hence this ai technology will increase the efficiency of the model.","PeriodicalId":135318,"journal":{"name":"2021 International Conference on Artificial Intelligence and Machine Vision (AIMV)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128380155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Malware Analysis using Ensemble Techniques: A Machine Learning Approach 使用集成技术的恶意软件分析:一种机器学习方法

2021 International Conference on Artificial Intelligence and Machine Vision (AIMV) Pub Date : 2021-09-24 DOI: 10.1109/aimv53313.2021.9670949

Sachin Sharma, S. Bharti

引用次数: 0