K-Nearest Neighbour Algorithm for Classification of IoT-Based Edge Computing Device

Arowolo, Micheal Olaolu; Ogundokun, Roseline Oluwaseun; Misra, Sanjay; Ozik, Jonathan; Kadri, Akeem Femi

doi:10.1007/978-3-030-80821-1_8

Cited by 7 publications

(3 citation statements)

References 60 publications

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“…We employed many metrics, including accuracy, precision, recall, and F1 score 39 − 41 , to evaluate the performance of our model. We determined the accuracy by dividing the total number of images by the percentage of correctly classified images; the formula 42 − 44 can be seen in Eq.…”

Section: Resultsmentioning

confidence: 99%

A novel smartphone application for early detection of habanero disease

Babatunde,

Ogundokun

et al. 2024

Sci Rep

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Habanero plant diseases can significantly reduce crop yield and quality, making early detection and treatment crucial for farmers. In this study, we discuss the creation of a modified VGG16 (MVGG16) Deep Transfer Learning (DTL) model-based smartphone app for identifying habanero plant diseases. With the help of the smartphone application, growers can quickly diagnose the health of a habanero plant by taking a photo of one of its leaves. We trained the DTL model on a dataset of labelled images of healthy and infected habanero plants and evaluated its performance on a separate test dataset. The MVGG16 DTL algorithm had an accuracy, precision, f1-score, recall and AUC of 98.79%, 97.93%, 98.44%, 98.95 and 98.63%, respectively, on the testing dataset. The MVGG16 DTL model was then integrated into a smartphone app that enables users to upload photographs, get diagnosed, and explore a history of earlier diagnoses. We tested the software on a collection of photos of habanero plant leaves and discovered that it was highly accurate at spotting infected plants. The smartphone software can boost early identification and treatment of habanero plant diseases, resulting in higher crop output and higher-quality harvests.

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Section: Resultsmentioning

confidence: 99%

A novel smartphone application for early detection of habanero disease

Babatunde,

Ogundokun

et al. 2024

Sci Rep

Self Cite

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“…In this phase, the R2-score and root mean square errors (RMSE) used to measure the imputation accuracy of the IMD-SNN and two other imputation models: imputation based KNN (I-KNN) [34] and imputation based multilayer perceptron (I-MLP) (i.e., more than one neural network (NN)) [7,35]. The I-KNN algorithm imputation missing value is performed by dividing a dataset into k distinguished clusters, in the first stage.…”

Section: Performance Evaluation Phasementioning

confidence: 99%

A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

Al-Jamali,

Al-Saedi,

Zarzoor

et al. 2023

IEEE Access

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Over the past decade, the Internet of Thing (IoT) devices have been deployed in wide-scale several applications to collect vast amount of data from different locations in a time-series manner. However, collected data may be missing or damaged due to several issues such as unreliable communications, faulty sensors and synchronization problem that decrees application accuracy. Therefore, a several imputation-based machine learning approaches have been suggested to handle this problem in IoT application. In this study, a new approach is proposed called impute missing data (IMD) based on multi-Spike Neural Network learning method called IMD-SNN, to increase the reliability of missing value imputation in IoT. The method consists of three phases: Inserting missing data, to evaluate the missing values based on the cumulative distribution function (CDF), the multi SNN phase to estimate missing data according to the timestamp and a performance evaluation phase to evaluate an imputation accuracy via made a comparison with two models: imputation based KNN (I-KNN) and Imputation based (I-MLP) model based on resource usage and imputation accuracy assessment metrics. The implementation results have been shown that IMD-SNN utilizes less energy usage in comparison with (I-MLP) model and I-KNN model and gives highest imputation accuracy in contrast with (I-MLP) model and I-KNN model. Also, the IMD-SNN model utilizes less memory usage and needs execution time less than I-MLP model. INDEX TERMS Internet of Thing (IoT), Spike Neural Network (SNN), Multilayer Perceptron (MLP), Root Mean Square Error (RMSE)

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“…Supporting widespread IoT applications is one of the primary causes of the need for increased power consumption [19]. Future network nodes, such as Internet of Things (IoT) gadgets and fog nodes, will have a lot of work to do, increasing power consumption [20,21]. Adopting Tera Hz communications, a short-range communications technology presents another energy-related difficulty for 6G networks.…”

Section: Introductionmentioning

confidence: 99%

Non-Orthogonal Multiple Access Enabled Mobile Edge Computing in 6G Communications: A Systematic Literature Review

et al. 2023

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Mobile edge computing (MEC) supported by non-orthogonal multiple access (NOMA) has recently gained a lot of interest due to its improved ability to lessen power consumption and MEC offload delay. In recent decades, the need for wireless communications has increased tremendously. Fifth-generation (5G) communications will soon be widely used and offer much more functionality than a fourth generation (4G). Between 2027 and 2030, an innovative wireless communication paradigm is known as the sixth generation (6G) system is projected to be introduced with the full help of artificial intelligence (AI). Advanced system capacity, higher data rate, lower latency, advanced security, and improved quality of service (QoS) than 5G systems are a few of the main challenges to resolve with 5G. The growing need for data rates in the sixth generation (6G) communication networks are being met by extraordinary technologies such as NOMA, Soft Computing (SC), and MEC. Owing to the massive attention to the NOMA-enabled MEC, there has been a significant spike in the number of papers published in this area, while more comprehensive studies and classifications are still needed. Using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines, the investigation reports a systematic literature review (SLR) of NOMA-enabled MEC. This survey also evaluates numerous pieces of literature prudently chosen over a multi-step procedure and meets the selection criteria described in the paper summarizing our review.

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K-Nearest Neighbour Algorithm for Classification of IoT-Based Edge Computing Device

Cited by 7 publications

References 60 publications

A novel smartphone application for early detection of habanero disease

A novel smartphone application for early detection of habanero disease

A New Imputation Technique Based a Multi-Spike Neural Network to Handle Missing Data in the Internet of Things Network (IoT)

Non-Orthogonal Multiple Access Enabled Mobile Edge Computing in 6G Communications: A Systematic Literature Review

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