Integrating artificial neural network and imperialist competitive algorithm (ICA), to predict the energy consumption for land leveling

Alzoubi, Isham; Delavar, Mahmoud Reza; Mirzaei, Farhad; Arrabi, Babak Nadjar

doi:10.1108/ijesm-01-2017-0003

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…AI can give farmers real-time output guidance. It may generate efficient and smart cities by recommending infrastructure development to hasten urbanization (Alzoubi et al 2017). These are some instances of how artificial intelligence may be used for the benefit of society.…”

Section: Ai For the Greater Goodmentioning

confidence: 99%

Contemporary issues in applying AI applications: challenges and opportunities

Prasad Babu,

Vasumathi

2023

Multidiscip. Rev.

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The broad acceptance and incorporation of artificial intelligence (AI) have significantly influenced a country's economy and human life. AI is used in industries, government, and even academic institutions to make decisions that have a direct and potential impact on lives. The integration of AI technology is a necessary step in developing intelligent manufacturing and the future smart factory. Improve diagnostic accuracy and screening time savings on business challenges are possible with artificial intelligence. This research helps to identify the superior AI technologies used in various areas, comprehend AI technologies and how they are applied, and determine AI opportunities and challenges. The deployment of AI in different sectors affects both producers and consumers. To reduce resistance of employees due to fear of technology, employers must make them aware of the usage of AI applications. One of the data challenges is a lack of quality of input data. The sharing of data and lack of trust are ethical challenges. There are challenges to the adoption of artificial intelligence all over the globe. Artificial intelligence-based start-ups face challenges due to the need for more suitable infrastructure. The increasing use of artificial intelligence can potentially change many aspects of human existence.

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Section: Ai For the Greater Goodmentioning

confidence: 99%

Contemporary issues in applying AI applications: challenges and opportunities

Prasad Babu,

Vasumathi

2023

Multidiscip. Rev.

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“…AI has potential to provide real-time guidelines to the farmers to increase production. It can be used for building efficient and smart cities by suggesting infrastructural development to accelerate urbanization (Alzoubi et al , 2017). These are a few examples of applications of AI for greater good.…”

Section: Opportunity For Indiamentioning

confidence: 99%

AI strategy of India: policy framework, adoption challenges and actions for government

Chatterjee

2020

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Purpose The purpose of this study is to provide recommendations for policy framework on artificial intelligence (AI) in India. Design/methodology/approach Studies have been conducted through focus group discussion and the other sources such as different company websites using AI, Indian Government strategy reports on AI, literature studies, different policies implemented on AI in different locations and other relevant documents. After those studies, a charter of recommendation has been provided. This will help the authority to frame the AI policy for India. Findings This study highlights that “National Strategy for AI” for India needs improvement to provide comprehensive inputs for framing policy on AI. This study also implies that focus is to be given on security, privacy issues including issues of governance. Research limitations/implications AI-related technology has immense potential toward the development of organizations. This study implies the necessity of framing a comprehensive policy on AI for India. If there is a comprehensive policy on AI for India, the Indian industries will derive many benefits. Practical implications This study provides inputs on how the industries of India can be benefitted with the help of AI and how R&D can develop the AI activities to harness maximum benefits from this innovative technology. Social implications AI-related policy will have appreciable influence on the society in terms of human–device interactions and communications. The policy framework on AI for India is expected to project far-reaching effects toward deriving benefits to the society. Originality/value This paper has taken a holistic and unique attempt to provide inputs to the policymakers for framing a comprehensive and meaningful policy on AI for India.

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“…If the actual output is not satisfied, the error will be transferred to the input layer through the hidden layer. In the next transmission process, the weight and bias of all input neurons will be adjusted until the error is reduced to a reasonable threshold [ 31 ], and finally, the training process is over. The flow diagram of the BP algorithm used in this study is shown in Table 1 .…”

Section: Design Of the Neural Network Modelmentioning

confidence: 99%

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

Wen

Zhang

Yin

et al. 2019

Sensors

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Recently, unmanned aerial vehicles (UAVs) have rapidly emerged as a new technology in the fields of plant protection and pest control in China. Based on existing variable spray research, a plant protection UAV variable spray system integrating neural network based decision making is designed. Using the existing data on plant protection UAV operations, combined with artificial neural network (ANN) technology, an error back propagation (BP) neural network model between the factors affecting droplet deposition is trained. The factors affecting droplet deposition include ambient temperature, ambient humidity, wind speed, flight speed, flight altitude, propeller pitch, nozzles pitch and prescription value. Subsequently, the BP neural network model is combined with variable rate spray control for plant protection UAVs, and real-time information is collected by multi-sensor. The deposition rate is determined by the neural network model, and the flow rate of the spray system is regulated according to the predicted deposition amount. The amount of droplet deposition can meet the prescription requirement. The results show that the training variance of the ANN is 0.003, and thus, the model is stable and reliable. The outdoor tests show that the error between the predicted droplet deposition and actual droplet deposition is less than 20%. The ratio of droplet deposition to prescription value in each unit is approximately equal, and a variable spray operation under different conditions is realized.

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Integrating artificial neural network and imperialist competitive algorithm (ICA), to predict the energy consumption for land leveling

Cited by 5 publications

References 23 publications

Contemporary issues in applying AI applications: challenges and opportunities

Contemporary issues in applying AI applications: challenges and opportunities

AI strategy of India: policy framework, adoption challenges and actions for government

Design of Plant Protection UAV Variable Spray System Based on Neural Networks

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