Analysis and Prediction of Crop Production in Andhra Region using Deep Convolutional Regression Network

Talasila, Vamsidhar; Prasad, Chitturi; Reddy, G. Vidyasagar; Aparna, Allada

doi:10.22266/ijies2020.1031.01

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…; The second is artificial intelligence methods, such as decision trees, artificial neural networks, support vector machines, evolutionary computing, etc. In order to examine the role of the textual quantitative indicators constructed by the authors in predicting financial distress, in order to ensure the robustness of the research conclusions, the authors chose the above two types of methods, the most widely used and most representative logistic regression and support vector machines are used for empirical analysis [8]. Logistic regression model (1)Among the traditional statistical methods of credit risk early warning, logistic regression is the most commonly used multivariate statistical method for modeling binary dependent variables, which can solve the problem of nonlinear classification, there are no specific requirements for the distribution of variables, and the accuracy of the judgment is relatively high.…”

Section: Choice Of Empirical Methodsmentioning

confidence: 99%

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Liyang

Yousif

Elkanj

2022

Applied Mathematics and Nonlinear Sciences

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In order to reduce the default rate of corporate bond market, the author proposes to use digital signal processing and multiple regression analysis to study the prediction system of financial distressed companies. First, design the research method, Logistic regression model is the most commonly used multivariate statistical method when modeling binary dependent variables, it can solve the problem of nonlinear classification, it has no specific requirements for the distribution of variables, and the accuracy of judgment is high. The author selects 32 financial ratios from the perspectives of solvency, operating ability, profitability, development ability, per share index, and risk level. Taking special treatment (ST) due to abnormal financial status as a sign of financial distress in listed companies, when selecting samples, the matching principle is adopted to select non-ST companies as matching samples. Two methods of logistic regression and support vector machine are used for empirical testing, and both in-sample testing and out-of-sample prediction are performed. The results show that when using the logistic regression method, the propensity to default indicator (TTD) reflected in the text content, it can indeed improve the out-of-sample prediction accuracy of the financial distress prediction model, and it is consistent with the in-sample test, this is mainly reflected in the reduction of the first type of error, that is, the probability of misjudging a financially distressed company as a normal company. Changes in the proportions have little effect on the relative importance of financial ratio variables when modeling with support vector machines, the propensity to default indicator (TTD) entered the top ten important variables in both ratios, and ranked fourth among all indicators when the ratio was 1:2, importance has increased significantly. From this it can be seen that, when using support vector machine to build a financial distress prediction model, the propensity to default indicator (TTD) has played an important role. In the case of using the support vector machine method, adding the default tendency indicator (TTD) reflected by the text information can also improve the accuracy of the financial distress prediction model.

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Section: Choice Of Empirical Methodsmentioning

confidence: 99%

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Liyang

Yousif

Elkanj

2022

Applied Mathematics and Nonlinear Sciences

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“…This avoids human interaction which manually and physically detects each machine's behavior. Wireless sensor nodes aim to connect things to the system through its senses ability such as based on movement [1], gas concentration [2], moisture [3], etc. However, sensor nodes are resource-constraint devices which have limitation in the power, storage, and computation.…”

Section: Introductionmentioning

confidence: 99%

Scalable Two-Dimensional Bloom Filter Membership Scheme on Massive Scale Wireless Sensor Networks

Afianti¹,

Asrini²,

Wijaya³

2021

IJIES

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Wireless sensor nodes help people to monitor any operational mechanism. It is important to be implemented in industrial automation in detecting unauthorized activity or malicious sensor nodes by the outsider. However, a massive number of devices or things which are needed to be controlled become a challenge. A fast mechanism to recognize a member or a valid device is needed to minimize production costs and avoid future procedural errors. It becomes an open problem for the implementation of massive scale Wireless Sensor Networks (WSN). Hence, a scalable two-dimensional Bloom filter is proposed in this paper to deal with this issue. The low computation complexity and the storage efficiency requirements by the two-dimensional Bloom filter are proven by the experimental results. The insertion, deletion and query procedure of the proposed method only need O (1), while the storage usage of two-dimensional Bloom filters not only lower than counting Bloom filter in average 131 bits difference but also approaches accommodative Bloom filter in about 10 bits difference. These two parameters support a fast membership scheme. Furthermore, the analysis of value initialization has been performed. To the best of our knowledge, the investigation of this parameter has not been addressed by other studies. This process aims to get the best scenario to increase scalability. Several recommendations for selecting dimension number has been stated which is useful for efficient storage usage and reduce false positive.

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Machine Learning and Food Security: Insights for Agricultural Spatial Planning in the Context of Agriculture 4.0

et al. 2022

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Climate change and global warming interconnected with the new contexts created by the COVID-19 pandemic and the Russia-Ukraine conflict have brought serious challenges to national and international organizations, especially in terms of food security and agricultural planning. These circumstances are of particular concern due to the impacts on food chains and the resulting disruptions in supply and price changes. The digital agricultural transition in Era 4.0 can play a decisive role in dealing with these new agendas, where drones and sensors, big data, the internet of things and machine learning all have their inputs. In this context, the main objective of this study is to highlight insights from the literature on the relationships between machine learning and food security and their contributions to agricultural planning in the context of Agriculture 4.0. For this, a systematic review was carried out based on information from text and bibliographic data. The proposed objectives and methodologies represent an innovative approach, namely, the consideration of bibliometric evaluation as a support for a focused literature review related to the topics addressed here. The results of this research show the importance of the digital transition in agriculture to support better policy and planning design and address imbalances in food chains and agricultural markets. New technologies in Era 4.0 and their application through Climate-Smart Agriculture approaches are crucial for sustainable businesses (economically, socially and environmentally) and the food supply. Furthermore, for the interrelationships between machine learning and food security, the literature highlights the relevance of platforms and methods, such as, for example, Google Earth Engine and Random Forest. These and other approaches have been considered to predict crop yield (wheat, barley, rice, maize and soybean), abiotic stress, field biomass and crop mapping with high accuracy (R2 ≈ 0.99 and RMSE ≈ 1%).

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Analysis and Prediction of Crop Production in Andhra Region using Deep Convolutional Regression Network

Cited by 3 publications

References 26 publications

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Prediction of corporate financial distress based on digital signal processing and multiple regression analysis

Scalable Two-Dimensional Bloom Filter Membership Scheme on Massive Scale Wireless Sensor Networks

Machine Learning and Food Security: Insights for Agricultural Spatial Planning in the Context of Agriculture 4.0

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