2,531 research outputs found
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields
Producción CientíficaSite-specific weed management and selective application of herbicides as eco-friendly techniques are still challenging tasks to perform, especially for densely cultivated crops, such as rice. This study is aimed at developing a stereo vision system for distinguishing between rice plants and weeds and further discriminating two types of weeds in a rice field by using artificial neural networks (ANNs) and two metaheuristic algorithms. For this purpose, stereo videos were recorded across the rice field and different channels were extracted and decomposed into the constituent frames. Next, upon pre-processing and segmentation of the frames, green plants were extracted out of the background. For accurate discrimination of the rice and weeds, a total of 302 color, shape, and texture features were identified. Two metaheuristic algorithms, namely particle swarm optimization (PSO) and the bee algorithm (BA), were used to optimize the neural network for selecting the most effective features and classifying different types of weeds, respectively. Comparing the proposed classification method with the K-nearest neighbors (KNN) classifier, it was found that the proposed ANN-BA classifier reached accuracies of 88.74% and 87.96% for right and left channels, respectively, over the test set. Taking into account either the arithmetic or the geometric means as the basis, the accuracies were increased up to 92.02% and 90.7%, respectively, over the test set. On the other hand, the KNN suffered from more cases of misclassification, as compared to the proposed ANN-BA classifier, generating an overall accuracy of 76.62% and 85.59% for the classification of the right and left channel data, respectively, and 85.84% and 84.07% for the arithmetic and geometric mean values, respectively
Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant
The Victoria Amazonica plant, often known as the Giant Water Lily, has the
largest floating spherical leaf in the world, with a maximum leaf diameter of 3
meters. It spreads its leaves by the force of its spines and creates a large
shadow underneath, killing any plants that require sunlight. These water
tyrants use their formidable spines to compel each other to the surface and
increase their strength to grab more space from the surface. As they spread
throughout the pond or basin, with the earliest-growing leaves having more room
to grow, each leaf gains a unique size. Its flowers are transsexual and when
they bloom, Cyclocephala beetles are responsible for the pollination process,
being attracted to the scent of the female flower. After entering the flower,
the beetle becomes covered with pollen and transfers it to another flower for
fertilization. After the beetle leaves, the flower turns into a male and
changes color from white to pink. The male flower dies and sinks into the
water, releasing its seed to help create a new generation. In this paper, the
mathematical life cycle of this magnificent plant is introduced, and each leaf
and blossom are treated as a single entity. The proposed bio-inspired algorithm
is tested with 24 benchmark optimization test functions, such as Ackley, and
compared to ten other famous algorithms, including the Genetic Algorithm. The
proposed algorithm is tested on 10 optimization problems: Minimum Spanning
Tree, Hub Location Allocation, Quadratic Assignment, Clustering, Feature
Selection, Regression, Economic Dispatching, Parallel Machine Scheduling, Color
Quantization, and Image Segmentation and compared to traditional and
bio-inspired algorithms. Overall, the performance of the algorithm in all tasks
is satisfactory.Comment: 45 page
Autonomous Vehicles an overview on system, cyber security, risks, issues, and a way forward
This chapter explores the complex realm of autonomous cars, analyzing their
fundamental components and operational characteristics. The initial phase of
the discussion is elucidating the internal mechanics of these automobiles,
encompassing the crucial involvement of sensors, artificial intelligence (AI)
identification systems, control mechanisms, and their integration with
cloud-based servers within the framework of the Internet of Things (IoT). It
delves into practical implementations of autonomous cars, emphasizing their
utilization in forecasting traffic patterns and transforming the dynamics of
transportation. The text also explores the topic of Robotic Process Automation
(RPA), illustrating the impact of autonomous cars on different businesses
through the automation of tasks. The primary focus of this investigation lies
in the realm of cybersecurity, specifically in the context of autonomous
vehicles. A comprehensive analysis will be conducted to explore various risk
management solutions aimed at protecting these vehicles from potential threats
including ethical, environmental, legal, professional, and social dimensions,
offering a comprehensive perspective on their societal implications. A
strategic plan for addressing the challenges and proposing strategies for
effectively traversing the complex terrain of autonomous car systems,
cybersecurity, hazards, and other concerns are some resources for acquiring an
understanding of the intricate realm of autonomous cars and their ramifications
in contemporary society, supported by a comprehensive compilation of resources
for additional investigation.
Keywords: RPA, Cyber Security, AV, Risk, Smart Car
Automated Reading Passage Generation with OpenAI's Large Language Model
The widespread usage of computer-based assessments and individualized
learning platforms has resulted in an increased demand for the rapid production
of high-quality items. Automated item generation (AIG), the process of using
item models to generate new items with the help of computer technology, was
proposed to reduce reliance on human subject experts at each step of the
process. AIG has been used in test development for some time. Still, the use of
machine learning algorithms has introduced the potential to improve the
efficiency and effectiveness of the process greatly. The approach presented in
this paper utilizes OpenAI's latest transformer-based language model, GPT-3, to
generate reading passages. Existing reading passages were used in carefully
engineered prompts to ensure the AI-generated text has similar content and
structure to a fourth-grade reading passage. For each prompt, we generated
multiple passages, the final passage was selected according to the Lexile score
agreement with the original passage. In the final round, the selected passage
went through a simple revision by a human editor to ensure the text was free of
any grammatical and factual errors. All AI-generated passages, along with
original passages were evaluated by human judges according to their coherence,
appropriateness to fourth graders, and readability
Traceable Ecosystem and Strategic Framework for the Creation of an Integrated Pest Management System for Intensive Farming
The appearance of pests is one of the major problems that exist in the growth of crops, as they can damage the production if the appropriate measures are not taken. Within the framework of the Integrated Pest Management strategy (IPM), early detection of pests is an essential step in order to provide the most appropriate treatment and avoid losses. This paper proposes the architecture of a system intensive farming in greenhouses featuring the ability to detect environmental variations that may favour the appearance of pests. This system can suggest a plan or treatment that will help mitigate the effects that the identified pest would produce otherwise. Furthermore, the system will learn from the actions carried out by the humans throughout the different stages of crop growing and will add it as knowledge for the prediction of future actions. The data collected from sensors, through computer vision, or the experiences provided by the experts, along with the historical data related to the crop, will allow for the development of a model that contrasts the predictions of the actions that could be implemented with those already performed by technicians. Within the technological ecosystems in which the Integrated Pest Management systems develop their action, traceability models must be incorporated. This will guarantee that the data used for the exploitation of the information and, therefore for the parameterization of the predictive models, are adequate. Thus, the integration of blockchain technologies is considered key to provide them with security and confidence
Tomato Flower Detection and Three-Dimensional Mapping for Precision Pollination
It is estimated that nearly 75% of major crops have some level of reliance on pollination. Humans are reliant on fruit and vegetable crops for many vital nutrients. With the intensification of agricultural production in response to human demand, native pollinator species are not able to provide sufficient pollination services, and managed bee colonies are in decline due to colony collapse disorder, among other issues. Previous work addresses a few of these issues by designing pollination systems for greenhouse operations or other controlled production systems but fails to address the larger need for development in other agricultural settings with less environmental control. In response to this crisis, this research aims to act as a vital first step towards the development of a more robust autonomous pollination system for agricultural crop production. The main objective of this research is to develop a flower detection and mapping system for a field crop setting. This research presents a method to detect and localize tomato flowers within a three-dimensional (3D) region. Tomato plants were grown in a raised-bed garden where images were collected of the overhead view of the plants. Images were then stitched together using a photogrammetry technique, accomplished by the Pix4Dmapper software, to form an orthomosaic and 3D representation of the raised-bed garden from a high spatial resolution aerial view. Various machine learning architectures were trained to detect tomato flowers from overhead images and were then tested on the orthomosaic images produced by the Pix4D software. The coordinates of the detected flowers in the orthomosaic were then compared to the 3D model representation to find approximate 3D coordinates for each of the flowers relative to a predefined origin. This research serves as a first step in autonomous pollination by presenting a way for machine vision and machine learning to be used to identify the presence and location of flowers on tomato crops. Future work will aim to expand flower detection to other crops varieties in varying field conditions
A Historic Waterfront Revitalisation Project in Tanjung Emas, Johor
This project addresses the importance of public open spaces in supporting the revitalisation of historic waterfront development along Tanjung Emas, Muar, Johor. At the domestic level, this place is popular as it attracts many visitors, particularly on weekends. Its location in the Royal Town of Bandar Maharani; thus, it plays an essential role in creating a catalyst for a sharp image of urban design elements. The distribution of many historical buildings such as Masjid Sultan Abu Bakar, Muar High School, and Muar District Court, reflecting the influence of colonial architecture adds to its colourful and vibrant image of an old town. Hence, the proposals which mainly cover the public open spaces along Tanjung Emas are expected to revitalise the image of Bandar Maharani. The projects involve mainly the uplifting the facilities of the children playground, provision of the water fountain, open theatre, pavilion, and floating café
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