Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune...
Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood.
In recent decades, global climate change has become one of the most critical environmental issues, leading to increased environmental and social concerns about the sustainability of logistics networks. This study prop...
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With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services...
With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results.
The research results of the Love wave propagation in the semi-space contact area and in a thin layer are presented. The dependences, which allow analyzing the effect of various conditions on the Love wave dispersion, ...
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Context: When developing software, it is vitally important to keep the level of technical debt down since it is well established from several studies that technical debt can, e.g., lower the development productivity, ...
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Context: When developing software, it is vitally important to keep the level of technical debt down since it is well established from several studies that technical debt can, e.g., lower the development productivity, decrease the developers' morale, and compromise the overall quality of the software. However, even if researchers and practitioners working in today's software development industry are quite familiar with the concept of technical debt and its related negative consequences, there has been no empirical research focusing specifically on how software managers actively communicate and manage the need to keep the level of technical debt as low as possible. Objective: This study aims to understand how software companies give incentives to manage Technical Debt. This is done by exploring how companies encourage and reward practitioners for actively keeping the level of technical debt down and whether the companies use any forcing or penalizing initiatives when managing technical debt. Method: In a first step, this paper reports the results of both an online survey provided quantitative data from 258 participants and interviews with 32 software practitioners. In a second step, this study set out to specifically provide a detailed assessment of additional and in-depth analysis of Technical Debt management strategies based on an encouraging mindset and attitude from both managers and technical roles to understand how, when and by whom such strategy is adopted in practice. Results: Our findings show that having a Technical Debt management strategy (specially based on encouragement) can significantly impact the amount of Technical Debt in the software. Conclusion: The result indicates that there is considerable unfulfilled potential to influence how software practitioners can further limit and reduce Technical Debt by adopting a strategy based explicitly on an encouraging mindset from managers where they also specifically dedicate time and resources for Technical De
The principles of solving the multicriteria problem of retail marketing for the rational choice of the location of trade enterprises (alternatives) based on the classical method of analysis of Saaty hierarchies, using...
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This paper describes a new approach on optimization of constraint satisfaction problems (CSPs) by means of substituting sub-CSPs with locally consistent regular membership constraints. The purpose of this approach is ...
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Since the last few decades, the prey-predator system delivers attractive mathematical models to analyse the dynamics of prey-predator interaction. Due to the lack of precise information about the natural parameters, a...
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Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various fea...
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ISBN:
(数字)9798350366846
ISBN:
(纸本)9798350366853
Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes—Low Performance Student, Average Student, and Smart Student—were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments.
As our skin is exposed to ultraviolet rays or dangerous chemicals, aberrant growth of skin cells happens which brings up undesirable conditions such as premature skin aging, transposition in skin texture, and the wors...
As our skin is exposed to ultraviolet rays or dangerous chemicals, aberrant growth of skin cells happens which brings up undesirable conditions such as premature skin aging, transposition in skin texture, and the worst-case scenario skin cancer. In the struggle to combat deadly skin cancer, machine learning can be a useful weapon to help dermatologists make better and clearer decisions while diagnosing patients. Despite promising results with numerous machine learning techniques, this field faces data inadequacy, more so the universally available datasets are subjected to data imbalances. In order to tackle the significant class imbalance present in datasets like the HAM10000 skin cancer dataset, this research introduces a class-weighted reward mechanism within the Deep Q-Learning framework that dynamically allocates higher positive rewards for the accurate classification of rare classes and imposes more substantial penalties for the incorrect classification of common classes. This strategy encourages the DQN agent to focus on underrepresented categories during the training process, thereby reducing bias towards majority classes. Quantitative assessment metrics such as Accuracy, Precision, F1-score, Specificity, and Sensitivity were used to evaluate the model. The results showed an accuracy of 97.97 %, sensitivity of 97.74 %, precision of 97.81 %, F1-Score of 97.70 %, and specificity of 97.83 % on a non-augmented dataset of HAM10000. Finally, the model performance was compared to that of already existing research work, and it had an upper hand with considerable differences over the existing ones.
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