Dynamic Control and Performance Evaluation of Microcontroller-Based Smart Industrial Heat Extractor

Essien, Joe

doi:10.37745/ejcsit.2013/vol11n35974

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The Correlation Matrix Plot, typically depicted as a heatmap in Figure 3, visually represents these correlations, with warmer colors indicating stronger positive correlations and cooler colors indicating stronger negative correlations. By analyzing this plot, insights into the interdependencies between variables can be gleaned, aiding in feature selection, model optimization, and ultimately, accurate crop categorization using the Random Forest algorithm [16]. Additionally, the Correlation Matrix Plot facilitates the identification of multicollinearity issues and redundant features, allowing for the refinement of predictive models and the enhancement of their performance in crop classification tasks.…”

Section: Figure 3 Correlation Matrix Plot Diagrammentioning

confidence: 99%

“…In the model-building process, the goal is to develop a predictive system that recommends the most suitable crop based on given environmental conditions [16]. This begins with thorough data preprocessing, which includes importing the dataset, performing exploratory data analysis to understand its structure and characteristics, and encoding categorical variables into numerical values for modeling.…”

Section: Model Buildingmentioning

confidence: 99%

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Data-Driven Framework for Crop Categorization using Random Forest-Based Approach for Precision Farming Optimization

Ayoola,

Essien,

Ogharandukun

et al. 2024

EJCSIT

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Making incorrect choices when selecting crops can result in substantial financial losses for farmers, primarily because of a limited understanding of the unique needs of each crop. Each farm possesses unique characteristics, influencing the effectiveness of modern agricultural solutions. Challenges persist in optimizing farming methods to maximize yield. This study aims to mitigate these issues by developing a data-driven crop classification and cultivation advisory system, leveraging machine learning algorithms and agricultural data. By analysing variables such as soil nutrient levels, temperature, humidity, pH, and rainfall, the system offers tailored recommendations for crop selection and cultivation practices. This approach optimizes resource utilization, enhances crop productivity, and promotes sustainable agriculture. The study emphasizes the importance of pre-processing data, such as handling missing values and normalizing features, to ensure reliable model training. Various machine learning models, including Random Forests, Bagging Classifier, and AdaBoost Classifier, were employed, demonstrating high accuracy rates in crop classification tasks. The integration of real-time weather data, market prices, and profitability analysis further refines decision-making, while a mobile application facilitates convenient access for farmers. By incorporating user feedback and continuous data collection, the system's performance can be continuously improved, offering precise and economically viable agricultural advice.

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Section: Figure 3 Correlation Matrix Plot Diagrammentioning

confidence: 99%

Section: Model Buildingmentioning

confidence: 99%

Data-Driven Framework for Crop Categorization using Random Forest-Based Approach for Precision Farming Optimization

Ayoola,

Essien,

Ogharandukun

et al. 2024

EJCSIT

View full text Add to dashboard Cite

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Enhancing Patients Outcomes and Infection Control through Smart Indoor Air Quality Monitoring Systems

Abalaka,

Essien,

Chimezie

et al. 2024

JCC

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Air pollution poses a critical threat to public health and environmental sustainability globally, and Nigeria is no exception. Despite significant economic growth and urban development, Nigeria faces substantial air quality challenges, particularly in urban centers. While outdoor air pollution has received considerable attention, the issue of indoor air quality remains underexplored yet equally critical. This study aims to develop a reliable, cost-effective, and user-friendly solution for continuous monitoring and reporting of indoor air quality, accessible from anywhere via a web interface. Addressing the urgent need for effective indoor air quality monitoring in urban hospitals, the research focuses on designing and implementing a smart indoor air quality monitoring system using Arduino technology. Employing an Arduino Uno, ESP8266 Wi-Fi module, and MQ135 gas sensor, the system collects real-time air quality data, transmits it to the ThingSpeak cloud platform, and visualizes it through a user-friendly web interface. This project offers a cost-effective, portable, and reliable solution for monitoring indoor air quality, aiming to mitigate health risks and promote a healthier living environment.

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Machine Learning Models for Heterogenous Network Security Anomaly Detection

Ogah,

Essien,

Ogharandukun

et al. 2024

JCC

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The increasing amount and intricacy of network traffic in the modern digital era have worsened the difficulty of identifying abnormal behaviours that may indicate potential security breaches or operational interruptions. Conventional detection approaches face challenges in keeping up with the ever-changing strategies of cyber-attacks, resulting in heightened susceptibility and significant harm to network infrastructures. In order to tackle this urgent issue, this project focused on developing an effective anomaly detection system that utilizes Machine Learning technology. The suggested model utilizes contemporary machine learning algorithms and frameworks to autonomously detect deviations from typical network behaviour. It promptly identifies anomalous activities that may indicate security breaches or performance difficulties. The solution entails a multi-faceted approach encompassing data collection, preprocessing, feature engineering, model training, and evaluation. By utilizing machine learning methods, the model is trained on a wide range of datasets that include both regular and abnormal network traffic patterns. This training ensures that the model can adapt to numerous scenarios. The main priority is to ensure that the system is functional and efficient, with a particular emphasis on reducing false positives to avoid unwanted alerts. Additionally, efforts are directed on improving anomaly detection accuracy so that the model can consistently distinguish between potentially harmful and benign activity. This project aims to greatly strengthen network security by addressing emerging cyber threats and improving their resilience and reliability.

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Dynamic Control and Performance Evaluation of Microcontroller-Based Smart Industrial Heat Extractor

Cited by 3 publications

References 18 publications

Data-Driven Framework for Crop Categorization using Random Forest-Based Approach for Precision Farming Optimization

Data-Driven Framework for Crop Categorization using Random Forest-Based Approach for Precision Farming Optimization

Enhancing Patients Outcomes and Infection Control through Smart Indoor Air Quality Monitoring Systems

Machine Learning Models for Heterogenous Network Security Anomaly Detection

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