While machine learning (ML) models for crop recommendation have demonstrated high predictive accuracy, a critical gap persists in their practical reliability: the omission of uncertainty quantification. Existing studi...
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Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cu...
Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%.
In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike...
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ISBN:
(数字)9798350370096
ISBN:
(纸本)9798350370102
In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last U.S. presidential elections. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user‐generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review and a fake one; even humans are often unable to tell the difference. In this paper, we introduce a new n‐gram model to detect automatically fake contents with a particular focus on fake reviews and fake news.
For Cardio-thoracic technology education program, the focus is on teaching pathophysiology, as well as the interpretation of electrocardiograms (ECG), through case studies. The curriculum includes instruction on treat...
For Cardio-thoracic technology education program, the focus is on teaching pathophysiology, as well as the interpretation of electrocardiograms (ECG), through case studies. The curriculum includes instruction on treatment options and the use of electrophysiology studies (EP studies) for tachycardia and cardiac ablation. A portion of e-learning resources for ECGs and EP studies are delivered via web-based platforms. However, to date, there have been no e-learning platforms that combine instruction on pathological ECGs, EP study, cardiac ablation, or simulations of EP which would provide a more comprehensive learning experience for students. Therefore, the purpose of this study was to create the electrophysiology study simulator program (EPSSIM), which serves as an e-learning platform for EP studies and ECG. The development started with gathering information about ECG from the medical records of patients at a public hospital. MATLAB was used to generate synthetic heart signals. Microsoft Visual Studio 2019 was used to construct the EPSSIM program. A total of 47 cardio-thoracic technology students who used the program completed a pre-test and post-test and questionnaires. The effectiveness and satisfaction of the EPSSIM program were evaluated. The results showed that participants' scores improved, but the improvement was restricted due to the short time that the students were able to use the system. The participants who completed the satisfaction survey generally indicated satisfaction with the effectiveness of EPSSIM. Overall, the use of the application was beneficial and the application could be successfully used in future to enhance users knowledge.
Internet of Things (IoT) is an environment in which digital equipment is augmented with sensors to share and receive data through network. When devices share data it can be effected by anomalies or any attack due to c...
Internet of Things (IoT) is an environment in which digital equipment is augmented with sensors to share and receive data through network. When devices share data it can be effected by anomalies or any attack due to corrupted data or by any other uncertainty and ambiguity in data. The data can also be corrupted through a damage in device. These attacks or anomalies damage the working of the IoT networks. The anomalous data can be detected through detection techniques however most anomaly detection techniques depend upon labelled data but for IoT datasets, usually class labels are not available. Labeling is performed by a manual process which is time consuming and also costly. As data in IoT increases day by day so there is a need to label and classify data for future unseen data. In this paper a hybrid algorithm is proposed in which both clustering and classification techniques are applied for automatic labeling and classifying on IoT dataset. The model contains two function. In the first phase k-means clustering is employed for labelling dataset instances as normal or anomalous. In the second phase labelled dataset is used to train Random Forest model to detect anomalies in IoT networks. The results show that the proposed model is detecting anomalies in IoT networks with an accuracy 98%, precision 98 %, recall 98%, and F-meausre 0.98%.
This research is an approach to intelligent vehicles with a LoRa communication system, LoRaWAN compatible for Long-Range and Outdoor Communication, but in this paper, we will test the ability of LoRa to handle autonom...
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This article proposes StrawberryTalk, an Internet of Things (IoT) platform for image-based strawberry disease detection. StrawberryTalk reuses the wall-mounted monitoring cameras without extra hardware cost. The contr...
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Computational music research plays a critical role in advancing music production, distribution, and understanding across various musical styles in the world. Despite the immense cultural and religious significance, th...
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This Participatory Action Design Research (PADR) study explores how persuasive technologies and the Internet of Things (IoT) can be used to encourage participation in recycling activities. By involving primary school ...
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ISBN:
(数字)9798331513054
ISBN:
(纸本)9798331513061
This Participatory Action Design Research (PADR) study explores how persuasive technologies and the Internet of Things (IoT) can be used to encourage participation in recycling activities. By involving primary school students in the design process of a sociotechnical solution through participatory methods, we aim to create intelligent and persuasive applications that effectively promote sustainable behaviors in the school environment. To address the objectives of this investigation, the PADR methodology was adapted to the context of persuasive technologies by incorporating some critical activities, such as defining target behaviors. We argue that applying a participatory action design research approach involving users in all the stages of the design process of persuasive technologies for sustainability can lead to engaging and intuitive solutions tailored to the specific needs of users in real-world contexts.
In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company’s business...
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In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company’s business goals. Given the large contribution of informationtechnology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of informationtechnology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal.
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