Optimizing Crop Yield in Agriculture using Data Mining and Machine Learning Techniques

Today’s age is Machine Learning (ML) and Data-Mining (DM) Techniques, as both techniques play a significant role in measuring vulnerability prediction accuracy. In the field of computer security, vulnerability is a fault that might be exploited as a risk artist that performs unlawful activities inside computer security. The attackers have several different fitting tools and they are taking advantage to operate software illegally and are using it for getting self-profit. Additionally, that helps to expose and identify the violence external. Weakness management remains a repeating exercise to identify, remediating, and justifying weaknesses. These exercises normally send software faults in computing security. The meaning of using weakness with the same risk might go to misperception. It is possible to have a major effect because of possible stability and the window of weakness presented a risk hole in the software and required to fruitfully finish and smoothly operate. A security room has to be set up (zero-day invaders). Software Security Faults stand serious among unavoidable complications in the realm of computer risk. In this study, we have provided a comprehensive review of three book chapters, more than a hundred research articles papers, and several associated papers of different work that have been studied within the capacity of SVA and discovery applying ML and data-mining techniques. The earlier work has been thoroughly read and an adequately comprehensive summary has been provided in table-1. ML techniques that can professionally handle these attacks and we expect the net result of this survey article to help indesigning the new detection model for identifying the above-mentioned attacks

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

Data mining and machine learning techniques can be applied to computer system design to aid in optimizing design decisions, improving system runtime performance. Data mining techniques have been investigated in the context of branch prediction. Specifically, a comparison of traditional branch predictor performance has been made to data mining algorithms. Additionally, the possiblity of whether additional features available within the architectural state might serve to further improve branch prediction has been evaluated. Results show that data mining techniques indicate potential for improved branch prediction, especially when register file contents are included as a feature set.

Predicting student academic performance has been an important research topic in Educational Data Mining (EDM) which uses machine learning and data mining techniques to explore data from educational settings. However measuring academic performance of students is challenging since students academic performance hinges on diverse factors. The interrelationship between variables and factors for predicting performance participate in complicated nonlinear ways. Traditional data mining and machine learning techniques may not be applied directly to these types of data and problems. In this study we develop a classification model to predict student performance using Deep Learning which automatically learns multiple levels of representation. We pre-train hidden layers of features layerwisely using an unsupervised learning algorithm sparse auto-encoder from unlabeled data, and then use supervised training for finetuning the parameters. We train model on a relatively large real world students dataset, and the experimental results show the effectiveness of the proposed method which can be applied into academic pre-warning mechanism.

Agriculture holds a predominant position in the growth of any country's prosperity. However, there exists a major threat in the crop yield due to unpredictable and uncontrolled climatic changes, traditional farming methods, and poor irrigation services. In the past, crop yield was predicted based on farmer's experience. At present, the challenge is to increase the crop yield to meet the needs of the growing population. Currently, Machine Learning (ML) techniques are being used in multiple fields to accomplish practical and productive solutions. There are various algorithms in ML based on classification, clustering, and neural networks which can be used to predict the crop yield. In this work, we propose a method based on K-Nearest Neighbors (KNN) algorithm which detects the soil quality and predicts the suitable crop for cultivation. We consider temperature and soil quality as inputs to our algorithm. In addition, our method suggests the fertilizer based on the crop predicted. The test results show that our method accurately predicts the crop selection and yield which helps the farmers to great extent.

Prediction of post-COVID chronic fatigue syndrome using data mining and machine learning techniques in Isfahan COVID cohort study.

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting it and comparing its forecasts against available historical data (1987-2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing its forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1-4 % in mid-season and over-estimated by 1 % at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space.

Car manufacturing sector represents a major focus in the development of the automotive industry. In this research paper, a proposed data mining application for the automotive manufacturing sector is explained and tested. The dataset was retrieved from the machine learning repository at the University of California, Irvine. This research paper aims to create a more reliable classifier for future object classification. Classification is an important technique in data mining. It is a supervised learning process that involves classifying an object into one of the predefined classes based on its attributes. In this paper, we use a large database containing 7 attributes and 1,728 instances. We compare the results of a simple classification technique (using the J48 decision tree inference algorithm and MONK) with results based on different parameters using WEKA (Waikato Environment Knowledge Analysis), a data mining tool. The results of the experiment show a comparison between three algorithms to see which is the best and least error-prone algorithm. The physical characteristics of a car viz . Engine location ,price, how many doors, stroke, city fuel consumption, and other factors determine a vehicle's performance. Therefore, developing such a classification, although a huge undertaking, is absolutely essential in the car industry. Machine learning techniques can help integrate computer-based systems to predict vehicle quality and improve system efficiency. Classification models were trained using 214 datasets. The predicted values of the classifiers were evaluated using 10-fold cross-validation, and the results were compared. Keywords: Data mining, Machine learning techniques, J48, decision trees, Car market, WEKA classification.

Data mining is a way of searching for information from large amounts of data for the purposes of various applications. Several techniques in data mining can be used for association, classification, clustering, prediction, and sequential modeling. Machine learning is used in medical science to help medical teams find out the condition of patients with heart disease. A lot of machine learning still has limited predictive capabilities, and is incompatible. This study uses different machine learning techniques, namely PSO-based SVM, Neural Network, Decision Tree, Naïve Bayes and SVM to assist in building, understanding and interpreting different models of heart disease diagnosis. The use of the pso-based svm algorithm in the prediction of heart disease shows a 100% greatest accuracy than the Decision Tree, only 88.68% and Naïve Bayes of 82.15%, Neural Network with an accuracy of. 95.71%, SVM with an accuracy of 99.71%. The results of this study are expected to be beneficial for the world of health and for researchers who use machine learning techniques.

Purpose Agricultural crops play a crucial role in food security and require commensurating environmental conditions, including adequate rainfall to ensure optimum growth. However, in the recent past, a reduction in the agriculture crop yield has been observed due to the deteriorating rainfall pattern. This paper aims to present a novel mathematical model to analyze the impact of rainfall on the growth of agriculture crops, as well as the impact of cloud seeding for promoting the rainfall, in case of less rainfall to ensure the optimum growth of agriculture crops. Design/methodology/approach The authors formulate a mathematical model assuming that the growth of agriculture crops wholly depends on rainfall. Also, agricultural crops can sustain and give optimal yields at a threshold of rainfall, after which rainfall negatively affects the growth rate of agriculture crops. Further, if the agriculture crops get insufficient rain to grow, the authors assume that cloud seeding agents are introduced in the regional atmosphere in proportion to the density of cloud droplets to increase rainfall. Findings This research shows that while cloud seeding agents boost crop yield, excessive rainfall poses significant risks on the yield. For any given value of π1 (conversion of cloud droplets into raindrops because of introduced cloud seeding agents), we have identified the threshold value of ϕ (introduction rate of cloud seeding agents into clouds) where crop yield can be maximized. Research limitations/implications This model highlights the delicate balance between rainfall and cloud seeding, offering policymakers valuable insights for maximizing agricultural crop yields. Originality/value This research provides strategies to mitigate crop loss due to unpredictable rainfall patterns.

Interactions between environmental factors are a major source of ecosystem complexity. Here we re‐analyse published data to define the type of relationships between drought and biotic and abiotic factors affecting plants and aphids from molecular to community scales. Out of 329 pair‐wise relationships, 55% were additive, 36% antagonistic and 9% synergistic. The type of relationship varied with plant and aphid trait; plant and aphid genotype; timing, duration and intensity of stress in relation to plant and aphid developmental stage; intra‐ and inter‐specific competition; and relations at higher trophic levels. We identify two aspects to make future research in this field more effective. First, frameworks are needed to scale from molecular cross‐talk between stresses to the relevant traits: crop yield in agriculture and fitness in nature. Second, experiments require a shift from a nominal (e.g., well‐watered, droughted) to a quantitative characterisation of stress, and realistic conditions to avoid artefacts from over‐simplified settings, for example, roots confined to pots, insects confined to cages. In the absence of predictive frameworks, interventions in agriculture and conservation are needed that are robust to the uncertainty in the type of relationship, that is, antagonistic, additive or synergistic.

Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources.

Usability evaluation is one of the essential aspects of software production. This evaluation should be done during the entire life cycle of a software application, from pre-production to production and post-production. However, the collection and evaluation of usability data can be a very challenging, time-consuming, and expensive task to be conducted manually, particularly for certain types of products and working conditions. These challenges may include the need to recruit participants fully engage and motivate them during evaluation, and factor in environmental conditions. Other challenges may include collecting data in real-world environments, especially when the users are geographically dispersed, minimizing evaluator and participant bias, and analyzing complex data sets, particularly when the volume of data is large. This research explores an alternative approach to automate the whole process of usability evaluation by utilizing data mining and machine learning techniques on data recorded in log files. The objective of this dissertation is to extract the value of usability-related metrics from user interactions and estimate usability measures in a quantitative manner. The use of log files for usability evaluation offers several advantages over traditional methods of evaluation, such as collecting data objectively without the need for subjective interpretation of the evaluator in some cases, creating a comprehensive view of user interactions with the possibility of identifying user behavioral patterns and trends that appear in large data sets, and collecting data from real-world scenarios instead of data from simulated scenarios. Other advantages include reducing evaluation costs, enabling remote data collection from anywhere in the world, which leads to the identification of location-dependent usability problems, and facilitating continuous monitoring over time, which leads to the identification of time-dependent usability issues. In this dissertation, Chapter II provides a comprehensive categorization, comparison, and summary of the pertinent usability evaluation techniques that utilize log files as input data in both academic and industrial research. Each method is examined carefully, and its respective strengths and weaknesses are highlighted to provide a systematic understanding of the advantages and limitations of each technique. Chapter III assesses the originality of the research questions and proposed solutions. Given the ongoing development and refinement of log file analysis tools and techniques by proficient teams in advanced corporations, and the substantial research already conducted in web usage mining, this dissertation compares its solution with prior works in these two domains. The similarities and disparities between them are evaluated to determine the uniqueness and value of

The increasing need for integrative and alternative medical therapies, especially in the aftermath of the COVID-19 epidemic, has emphasized the importance of medicinal plants in worldwide healthcare. These plants, which contain abundant bioactive secondary metabolites, provide a sustainable and cost-effective option for medicinal, adaptogenic, and immune-boosting purposes. Blooming medicinal plants that exist are at risk of becoming extinct because of excessive harvesting, deforestation, and wildfires. Medicinal plants have complex physiological defenses against stress, which are strengthened by their symbiotic relationship with endophytes. Endophytes are microbial colonies that live within plant tissues without causing harm and play a vital role in maintaining the health of plants by helping them to tolerate stress, promoting development, acquiring nutrients, synthesizing phytohormones, breaking down toxic substances, and improving plant resistance to environmental pressures such as high salt levels, lack of water, and exposure to heavy metals. In addition, endophytes have a role in managing biotic stress by engaging in antibiosis, synthesizing lytic enzymes, producing secondary metabolites, and regulating hormones. Their function in preserving the health and well-being of the host, ensuring proper nutrition intake, and enhancing resistance against pathogens highlights their potential as agents for biological control and biofertilization, providing a safer option compared to chemical pesticides. Endophytic inoculants have the potential to significantly transform crop yield in agriculture by reducing the impact of abiotic problems and improving soil health. This review critically evaluates causal studies and recent omics-based advances, highlighting their crucial significance for sustainable bioinoculant development and practical applications in climate-resilient agriculture.

This is a proposed business model for a systems approach in adapting or optimizing plants towards novel products that briefly explains the growth and recent trends in plant systems using three modeling approaches: L-systems, Dynamical-biosystems analysis and cellular automata. The model discusses some of the attempts made by scientists in this field such as model development in Faba bean crops and the auto regulation in nodulation of plants and some other simulated models (ALAMEDA). Its application in horticulture or life style industry is proposed based on the meristem model applied in the roots of Zea mays. L. A grid based approach that effectively simulates and models competition among chosen species of plants on a simulated environment helps in the study of species survival amidst competition from other species, threat from pests, weeds etc. A model for the ecological risk assessment enables the study of impacts of a newly introduced plant variety on the environment and the implications on plants by the environment that helps in decision making and public acceptance. There are attempts in building the virtual cells, by choosing to apply living cell conditions with some basic cell components working effectively, leading to a thrust in the field of systems biology where this would be an application on all kind of cells in any organisms. A plant competition model in an agronomic perspective uses some of the plant models that help in increase of crop yield in agriculture.