Diagnosing Pilgrimage Common Diseases by Interactive Multimedia Courseware

Mohammed, Mazin Abed; Ali, Itimad Raheem; Obaid, Omar Ibrahim

doi:10.21123/bsj.2022.19.1.0168

Cited by 10 publications

(3 citation statements)

References 27 publications

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“…The ADDIE model was selected due to its effectiveness and systematic approach to addressing issues related to instructional media from various learning sources (Nenohai, Nubatonis, & Samo, 2021). The ADDIE model also supports active participation from students during the learning process and challenges educators to be more creative in creating and producing viable and effective instructional media (Mohammed, Ali, & Obaid, 2022).…”

Section: Methodsmentioning

confidence: 99%

Developing Canva-Based Learning Media on Maps and Class Layout for Third Graders of Elementary School

Friendha Yuanta,

Diyas Age Larasati

2023

EHDJ

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This study aimed to develop Canva-Based Learning Media to produce feasible and effective learning media products on Maps and Class Layout for third graders at SDN Dukuh Kupang V Surabaya. The study used the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). Data were collected using a questionnaire instrument and analyzed through validity tests conducted by media experts, content experts, the target audience, and pre-tests and post-tests for students. The results showed that the animated media is categorized as appropriate, with validity results of 90% from media experts, 92,5% from content experts, and 91.25% from participant tests. Furthermore, the pre-test and post-test results showed an 8% improvement, signifying its effectiveness as a learning medium for third graders at SDN Dukuh Kupang V Surabaya.

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Section: Methodsmentioning

confidence: 99%

Developing Canva-Based Learning Media on Maps and Class Layout for Third Graders of Elementary School

Friendha Yuanta,

Diyas Age Larasati

2023

EHDJ

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“…where, 𝑎𝑎𝑎𝑎 𝑖𝑖𝑖𝑖 -obtained output from neuron i in the output layer and n -number of classes in the output layer. (15) Dropout layers are used to reduce the complexity and prevents from overfitting. The regularization rate for this dropout layer is 0.5 that can drop some neuron during training.…”

Section: 5mentioning

confidence: 99%

“…The DNN is comprised of two stages [15,22] such as forward propagation and backward propagation. In forward propagation, the inputs are multiplied with the weights and bias which is assigned to each neuron to travel towards the hidden layer.…”

Section: Classification Using Dnnmentioning

confidence: 99%

Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith

Sheikdavood¹,

Bala²

2023

ITC

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Polycystic ovary syndrome (PCOS) is a disorder in the female ovary caused because of reproductive age group hormonal changes. PCOS is a different follicle that is formed in the ovary and is termed an endocrine disorder. This disorder’s effects are often linked with clinical symptoms such as arteries, acne, hirsutism, diabetes, cardiovascular disease, and chronic infertility. It is mainly associated with type 2 diabetes, obesity with high cholesterol. This must be diagnosed at an earlier stage for avoiding other related diseases. To ensure infertility, various kinds of ovulatory failures must be significantly diagnosed and recognized. The physicians manually determine the PCOS using ultrasound images, but it is inefficient to declare whether it is a simple cyst, PCOS, or cancer cyst. This manual detection is prone to trying errors. In this paper, PCOS detection is performed through a series of processes such as preprocessing, segmentation, feature selection, and classification. The speckle noise is removed in preprocessing, and the images are enhanced for further processing. The proposed improved adaptive K-means with reptile search algorithm (IAKmeans-RSA) has been utilized for cyst segmentation and follicles recognition. The relevant features from the segmented images are extracted using a convolutional neural network (CNN). Finally, the classification is performed using the Deep Neural Network (DNN) approach. The proposed system efficiently diagnosed PCOS through cyst detection from the input images. The algorithm’s efficiency compared with existing methods shows that the proposed model is superior in segmenting and diagnosing PCOS.

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Cardiac disease diagnosis using feature extraction and machine learning based classification with Internet of Things(IoT)

Muthulakshmi

Lakshmipathy²,

Vijayan

et al. 2021

Concurrency and Computation

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The applications of IoT have been employed in diverse domains like industries, clinical care, and farming, and so forth. Nowadays, the constitution of this technology is more prevalent in clinical observation, where the wearable devices have stimulated the development of the Internet of Medical Things (IoMT). In the process of reducing the death rate, it is necessary to detect the disease at an earlier stage. The cardiac disease prediction is a major defect in the examination of the dataset in clinics. The research proposed aims to recognize the important cardiac complaint prediction characteristics by utilizing machine‐learning methodologies. Numerous projects have been established regarding the diagnosis of cardiac complaints, which results in low accuracy rate. Thus, for improving the accuracy of prediction and for cardiac complaint investigation this article utilized a fuzzy c‐means neural network (FNN) and a deep convolution neural network for feature extraction. From the clinical dataset, data were obtained for the risk prediction of cardiac complaints that includes blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, and so forth. The hearts condition is recognized by categorizing the sensor data received by FNN. The evaluation performances were carried out and the results revealed that FNN is good in predicting the cardiac complaints. In addition to this, the proposed model achieves better accuracy than the other approaches through the demonstration of simulation results. The proposed approach attains the accuracy rate of 86.4% and F1‐score of 97%, precision 76.2%, and 64.6% of FPR.

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Diagnosing Pilgrimage Common Diseases by Interactive Multimedia Courseware

Cited by 10 publications

References 27 publications

Developing Canva-Based Learning Media on Maps and Class Layout for Third Graders of Elementary School

Developing Canva-Based Learning Media on Maps and Class Layout for Third Graders of Elementary School

Polycystic Ovary Cyst Segmentation Using Adaptive K-means with Reptile Search Algorith

Cardiac disease diagnosis using feature extraction and machine learning based classification with Internet of Things(IoT)

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