Quantum GIS Based Descriptive and Predictive Data Analysis for Effective Planning of Waste Management

Imran,; Ahmad, Shabir; Kim, Do‐Hyeun

doi:10.1109/access.2020.2979015

Cited by 65 publications

(27 citation statements)

References 17 publications

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“…In the current scenario, food industries are considering the benefits of AI-based systems. Under the AI-based system, computer vision and deep-learning algorithm is very important and used to investigate the sequence of information or data received by the AI-based agents to trace the progress of crop and soil health [10,[12][13][14][15][16][17][18][19][20][21][22]. e computerized systems are used to make available clients with a sagacity of their soil's strengths and weaknesses.…”

Section: Soil Monitoringmentioning

confidence: 99%

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Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

Kumar

Rawat

Mohd

et al. 2021

Journal of Food Quality

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The food processing and handling industry is the most significant business among the various manufacturing industries in the entire world that subsidize the highest employability. The human workforce plays an essential role in the smooth execution of the production and packaging of food products. Due to the involvement of humans, the food industries are failing to maintain the demand-supply chain and also lacking in food safety. To overcome these issues of food industries, industrial automation is the best possible solution. Automation is completely based on artificial intelligence (AI) or machine learning (ML) or deep learning (DL) algorithms. By using the AI-based system, food production and delivery processes can be efficiently handled and also enhance the operational competence. This article is going to explain the AI applications in the food industry which recommends a huge amount of capital saving with maximizing resource utilization by reducing human error. Artificial intelligence with data science can improve the quality of restaurants, cafes, online delivery food chains, hotels, and food outlets by increasing production utilizing different fitting algorithms for sales prediction. AI could significantly improve packaging, increasing shelf life, a combination of the menu by using AI algorithms, and food safety by making a more transparent supply chain management system. With the help of AI and ML, the future of food industries is completely based on smart farming, robotic farming, and drones.

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Section: Soil Monitoringmentioning

confidence: 99%

“…In the literature, so many studies [13][14][15][16][17][18][19][20] were found on smart farming and noticed that by using these modern tools, resources can be utilized in an optimized manner and the income can be maximized. Also, there are some specific…”

Section: Soil Monitoringmentioning

confidence: 99%

Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

Kumar

Rawat

Mohd

et al. 2021

Journal of Food Quality

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“…Similar approaches are backed by Boskovic and Jovicic [34] and Erfani et al [35] to solve the case studies based on urban locations in Serbia and in Iran, respectively. A more young study has been conducted by Imran et al [36] for Jeju Island. In this study, quantum GIS is used to investigate the behavior of people towards waste disposal and the prediction of waste for a certain residential area using predictive analytics.…”

Section: Related Workmentioning

confidence: 99%

Optimal Route Recommendation for Waste Carrier Vehicles for Efficient Waste Collection: A Step Forward Towards Sustainable Cities

et al. 2020

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The exponentially growing population, urbanization, and economic development have led to the rising generation of municipal solid waste. Municipal solid waste management is thus a significant hurdle for urban societies as it consumes a large chunk of public funds, and, when mishandled, it can lead to environmental and social hazards. Some of the prerequisites required for effective waste management are the monitoring of bins, timely collection of bins, and prioritization of those areas which produce more solid waste. In this paper, we propose an optimal route recommendation system for waste carriers vehicles to effectively collect solid waste based on the profile of a particular area. This article contributes with a multi-objective optimization approach to generate a route by minimizing the route distance and maximizing the amount of waste. Then, a family of evolutionary methods is employed to solve the proposed objective function and find the optimal route for waste carrier vehicles. The experiment is carried out on the real-world solid waste data of Jeju Island, South Korea. The data is processed to predict the behavior of people of a specified grid location in terms of waste disposal. Therefore, the recommendation system caters to the predicted waste across a set of bins inside the area, and considering the constraints such as total allowed distance and time, proposes a route that is best in terms of distance (fuel consumption) and waste collection. Different use cases are illustrated to signify the proposed system, and results indicate that it can be a step forward for the implementation of smart cities, which is the goal of Jeju Island. INDEX TERMS Waste management, route optimization, smart cities, sustainable development, green projects, Jeju Island.

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“…The SVR is a well-known learning algorithm and works similarly as the support vector Machine (SVM) for classification with some minor differences [16], [17]. The main advantage of SVM/SVR is that it is not model dependent, and its guarantees to give the optimal prediction [18]. Random Forest (RF) is another supervised learning algorithm for the regression model.…”

Section: Introductionmentioning

confidence: 99%

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

et al. 2020

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Large scale data and predictive analytics are the most challenging tasks in the field of academic data mining. Academic libraries are a great source of information and knowledge to provide a wide range of services to meet end-user requirements. Due to the rapid changes in the educational environment and availability of huge library rental book data, it is required to utilize data mining and machine learning techniques in the context of the academic library to extract and analyze underlying knowledge from rental book data, which is important to facilitate library administration to drive better future decisions to improve and manage library resources effectively. These are the following resources, such as managing future demands of the library books, selection and arrangement of the books, operational efficiency, and also improve the quality of interaction between the library and end-users, etc. This work uses and analyzes a real dataset collected from the library of Jeju National University, the Republic of Korea. The dataset contains 2,211,413 rental book records including 173671 unique book records, 57203 unique number of the rental user, and 78 data parameters. In this paper, we propose a novel model to analyze and predict library rental book data to facilitates library administration in order to plan and manage library resources effectively and provide better services to end-users. The proposed model consists of two different modules; library data analysis and prediction modules. Firstly, we use data mining techniques to analyze and extract useful underlying patterns from library rental book data, which can lead to plan and manage library resources effectively. Secondly, a novel prediction model is proposed based on Deep Neural Network (DNN), Support Vector Regressor (SVR), and Random Forest (RF) to predict future usage of the academic libraries rental books. The performance results of the implemented regression models are evaluated in terms of MAE, MSE, and RMSE. In this paper, it is found that the DNN model performs significantly better than SVR and RF. The experimentation results show that the proposed model improves the future usage of library books to facilitate library administration to plan and manage library resources effectively. Based on the proposed model results, the academic library administration can easily plan and manage resources effectively to provide quality services to end-users. INDEX TERMS Data mining, academic libraries, big data, machine learning, predictive analysis.

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Quantum GIS Based Descriptive and Predictive Data Analysis for Effective Planning of Waste Management

Cited by 65 publications

References 17 publications

Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

Optimal Route Recommendation for Waste Carrier Vehicles for Efficient Waste Collection: A Step Forward Towards Sustainable Cities

Toward Effective Planning and Management Using Predictive Analytics Based on Rental Book Data of Academic Libraries

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