Towards automatic waste containers management in cities via computer vision: containers localization and geo-positioning in city maps
به سمت مدیریت خودکار ظروف زباله در شهرها از طریق بینایی کامپیوتری: محلی سازی ظروف و موقعیت جغرافیایی در نقشه های شهر-2022
This paper describes the scientific achievements of a collaboration between a research group and the waste management division of a company. While these results might be the basis for several practical or commercial developments, we here focus on a novel scientific contribution: a methodology to automatically generate geo- located waste container maps. It is based on the use of Computer Vision algorithms to detect waste containers and identify their geographic location and dimensions. Algorithms analyze a video sequence and provide an automatic discrimination between images with and without containers. More precisely, two state-of-the-art object detectors based on deep learning techniques have been selected for testing, according to their perfor- mance and to their adaptability to an on-board real-time environment: EfficientDet and YOLOv5. Experimental results indicate that the proposed visual model for waste container detection is able to effectively operate with consistent performance disregarding the container type (organic waste, plastic, glass and paper recycling,…) and the city layout, which has been assessed by evaluating it on eleven different Spanish cities that vary in terms of size, climate, urban layout and containers’ appearance.
keywords: Waste container localization | Deep Learning | Computer Vision | Object detection
Recent developments of content-based image retrieval (CBIR)
پیشرفت های اخیر بازیابی تصاویر مبتنی بر محتوا (CBIR)-2021
With the development of Internet technology and the popularity of digital devices, Content-Based Image Retrieval (CBIR) has been quickly developed and applied in various fields related to computer vision and artificial intelligence. Currently, it is possible to retrieve related images effectively and efficiently from a large scale database with an input image. In the past ten years, great efforts have been made for new theories and models of CBIR and many effective CBIR algorithms have been established. In this paper, we present a survey on the fast developments and applications of CBIR theories and algorithms during the period from 2009 to 2019. We mainly review the technological developments from the viewpoint of image representation and database search. We further summarize the practical applications of CBIR in the fields of fashion image retrieval, person re-identification, e-commerce product retrieval, remote sensing image retrieval and trademark image retrieval. Finally, we discuss the future research directions of CBIR with the challenge of big data and the utilization of deep learning techniques.© 2020 Elsevier B.V. All rights reserved.
Keywords: Content-based image retrieval | Image representation | Database search | Computer vision | Big data | Deep learning
A real-time deep-learning approach for filtering Arabic low-quality content and accounts on Twitter
یک رویکرد یادگیری عمیق در زمان واقعی برای فیلتر کردن عربی با کیفیت پایین محتوا و حساب های کاربری در توییتر-2021
Social networks have generated immense amounts of data that have been successfully utilized for research and business purposes. The approachability and immediacy of social media have also allowed ill-intentioned users to perform several harmful activities that include spamming, promoting, and phishing. These activities generate massive amounts of low-quality content that often exhibits duplicate, automated, inappropriate, or irrelevant content that subsequently affects users’ satisfaction and imposes a significant challenge for other social media-based systems. Several real-time systems were developed to tackle this problem by focusing on filtering a specific kind of low-quality content. In this paper, we present a fine-grained real-time classification approach to identify several types of lowquality tweets (i.e., phishing, promoting, and spam tweets) written in Arabic. The system automatically extracts textual features using deep learning techniques without relying on hand-crafted features that are often time-consuming to be obtained and are tailored for a single type of low-quality content. This paper also proposes a lightweight model that utilizes a subset of the textual features to identify spamming Twitter accounts in a real-time setting. The proposed methods are evaluated on a real-world dataset (40, 000 tweets and 1, 000 accounts), showing superior performance in both models with accuracy and F1-scores of 0.98. The proposed system classifies a tweet in less than five milliseconds and an account in less than a second.
keywords: محتوای کم کیفیت در شبکه های اجتماعی | حساب های اسپم | سیستم تشخیص زمان واقعی | تکنیک های یادگیری عمیق | Low-quality content in social networks | Spam accounts | Real-time detection system | Deep learning techniques
A review on 3D image reconstruction on specific and generic objects
A review on 3D image reconstruction on specific and generic objects-2021
With the emergence of various techniques involved in deep learning the researchers of computer vision tends to focus on the strategies such as object recognition and segmentation of image. This has inclined to accomplish the deep learning techniques in 3D reconstruction of both specific and generic objects. As the space for reconstruction of 3D images either in single or multi view has envisioned the researchers to concentrate on the available technologies used for reconstruction. With the available built in methods and technologies in deep learning, the performance of the proposed methods were reviewed and analyzed using several parameters. As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Several methods and their importance were also discussed along with the challenges encountered and with further research directions. This paper critically analyzes the various 3D Shaped Representations, 3D Data Network Architectures, Depth Estimation methods, Multi View Representations and the Data Representation Techniques.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Reconstruction | Computer vision | Segmentation | Object recognition | 3D deep learning | Shape representations
Optimal carbon storage reservoir management through deep reinforcement learning
مدیریت بهینه ذخیره مخزن کربن از طریق یادگیری تقویتی عمیق-2020
Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decisionmaking problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.
Keywords: Reinforcement learning | Multistage decision-making | Deep autoregressive model | Deep Q network | Surrogate modeling | Markov decision process | Geological carbon sequestration
Interactive Transport Enquiry with AI Chatbot
استعلام حمل و نقل تعاملی با هوش مصنوعی Chatbot-2020
Public transportation is used efficiently by millions of people all over the world. People tend to travel to different places and at certain times they may feel completely lost in a new place. Our chatbot comes to rescue at this time. A Chatbot is often described as one of the most promising tools for communication between humans and machines using artificial intelligence. It is a software application that is used to conduct an online chat conversation via text by using natural language processing (NLP) and deep learning techniques. It provides direct contact with a live human agent in the form of GUI. This AI chatbot confirms the current location and the final destination of the user by asking a few questions. It examines the user’s query and extracts the appropriate entries from the database. The deep learning techniques that are used in this chatbot are responsible for understanding the user intents accurately to avoid any misconceptions. Once the user’s intention has been recognized, the chatbot provides the most relevant response for the user’s query request. Then the user gets all the information about the bus names along with their numbers so that the person can travel safely to the desired location. Our chatbot is implemented in pythons Keras library and used Tkinter for GUI.
Keywords: artificial intelligence | chatbot | natural language processing | deep learning | Keras | GUI | Tkinter
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions. In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis.
Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics
An empirical case study on Indian consumers sentiment towards electric vehicles: A big data analytics approach
یک مطالعه موردی تجربی در مورد احساسات مصرف کنندگان هندی نسبت به وسایل نقلیه برقی: یک رویکرد تحلیل داده های بزرگ-2020
Today, climate change due to global warming is a significant concern to all of us. Indias rate of greenhouse gas emissions is increasing day by day, placing India in the top ten emitters in the world. Air pollution is one of the significant contributors to the greenhouse effect. Transportation contributes about 10% of the air pollution in India. The Indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles. But, success depends on consumers sentiment, perception and understanding towards Electric Vehicles (EV). This case study tried to capture the feeling, attitude, and emotions of Indian consumers towards electric vehicles. The main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for them), marketers (for determining what features should be advertised) and manufacturers (for deciding what features should be improved) using Deep Learning techniques (e.g Doc2Vec Algorithm, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN)). Due to the very nature of social media data, big data platform was chosen to analyze the sentiment towards EV. Deep Learning based techniques were preferred over traditional machine learning algorithms (Support Vector Machine, Logistic regression and Decision tree, etc.) due to its superior text mining capabilities. Two years data (2016 to 2018) were collected from different social media platform for this case study. The results showed the efficiency of deep learning algorithms and found CNN yield better results in-compare to others. The proposed optimal model will help consumers, designers and manufacturers in their decision-making capabilities to choose, design and manufacture EV.
Keywords: Electric vehicles | Deep learning | Big data | Sentiment analysis | India
Cryptocurrency forecasting with deep learning chaotic neural networks
پیش بینی cryptocurrency با یادگیری عمیق شبکه های عصبی پر هرج و مرج-2019
We implement deep learning techniques to forecast the price of the three most widely traded digital currencies i.e., Bitcoin, Digital Cash and Ripple. To the best of our knowledge, this is the first work to make use of deep learning in cryptocurrency prediction. The results from testing the existence of non- linearity revealed that the time series of all digital currencies exhibit fractal dynamics, long memory and self-similarity. The predictability of long-short term memory neural network topologies (LSTM) is signif- icantly higher when compared to the generalized regression neural architecture, set forth as our bench- mark system. The latter failed to approximate global nonlinear hidden patterns regardless of the degree of contamination with noise, as they are based on Gaussian kernels suitable only for local approximation of non-stationary signals. Although the computational burden of the LSTM model is higher as opposed to brute force in nonlinear pattern recognition, eventually deep learning was found to be highly efficient in forecasting the inherent chaotic dynamics of cryptocurrency markets.
Keywords: Digital currencies | Deep learning | Fractality | Neural networks | Chaos | Forecasting
Projecting Australias forest cover dynamics and exploring influential factors using deep learning
پیش بینی پویایی پوشش جنگلی در استرالیا و کشف عوامل مؤثر با استفاده از یادگیری عمیق-2019
This study presents the first application of deep learning techniques in capturing long-term, time-continuous forest cover dynamics at a continental scale. We developed a spatially-explicit ensemble model for projecting Australias forest cover change using Long Short-Term Memory (LSTM) deep learning neural networks applied to a multi-dimensional, high-resolution spatiotemporal dataset and run on a high-performance computing cluster. We further quantified the influence of explanatory variables on the spatiotemporal dynamics of continental forest cover. Deep learning greatly outperformed a state-of-the-art spatial-econometric model at continental, state, and grid-cell scales. For example, at the continental scale, compared to the spatial-econometric model, the deep learning model improved projection performance by 44% (root-mean-square error) and 12% (pseudo Rsquared). The results illustrate the robustness and effectiveness of the LSTM model. This work provides a reliable tool for projecting forest cover and agricultural production under given future scenarios, supporting decisionmaking in sustainable land development, management, and conservation.
Keywords: Long short-term memory | Deep learning | Forest cover change | Spatiotemporal data | Projections | Deforestation