White Blood Cell (WBC) differential counting via blood film examination is a critical diagnostic tool for rapid and accurate clinical decision-making in systemic inflammation in animals. To address the need for automated and reliable WBC classification, this study proposes ConTexT-Net, a single-modality multi representation fusion network that achieves robust WBC differentiation by integrating contour- and texture-based representations. The proposed methodology consists of two primary stages: first, contour information is extracted using Canny edge detection to outline nuclear boundaries, and texture information is captured through adaptive thresholding to highlight fine-grained intracellular patterns. Subsequently, the preprocessed representations are fused with the RGB appearance branch using late feature-level fusion to classify WBCs. Experimental validation demonstrates that ConTexT-Net significantly outperforms a DenseNet-121 baseline, achieving an accuracy gain of 1.7 percentage points$(88.9 \%$vs.$87.2 \%)$. This enhanced performance supports the model's value as a reliable decision-support tool for veterinary clinical practice.

Bees play a crucial role in human food production and ecosystem maintenance. However, they face a global crisis characterized by significant population decline, including colony collapse disorder, which threatens their survival. Therefore, the development of precision beekeeping Internet of Things (IoT) systems is pivotal for enhancing bee colony strength. This study analyzes and categorizes the research focusing on the colony strength into two main areas based on colony activity and threat detection. Research on colony activity aids beekeepers to effectively manage their hives by analyzing behaviors, such as internal status, swarming, and traffic. In threat detection, research focuses on identifying critical predators, such as hornets and varroa destructor, that significantly affect colony survival. This study conducts a comprehensive literature review, thoroughly presenting findings on the evolution and diversification of methodologies aimed at enhancing colony strength. By delving into various innovative approaches and assessing their effectiveness, this review highlights key developments that have significantly contributed to improving the resilience of bee populations against emerging threats. Furthermore, this study identifies the limitations of current research and proposes future research directions focused on enhancing the accuracy of bee behavior analysis and threat detection to improve colony strength and productivity.

In veterinary medicine, the analysis of blood smears is crucial for diagnosing diseases such as systemic inflammatory response syndrome (SIRS) and sepsis, necessitating the identification and classification of white blood cells. Traditionally, this analysis is performed manually by observers, a process that is not only time-consuming and labor-intensive but also prone to variability in results between different observers. To address these challenges, this study introduces a two-stage framework that automates the detection and classification of white blood cells in smear images. Utilizing the YOLO-v8 model to detect all intact cells and the DenseNet model for classifying six distinct cell types, the framework aims to streamline the diagnostic process. Experimental results for the proposed two-stage framework demonstrate a mAP@50 of 0.964 for white blood cells detection and an accuracy of 0.836 for classification, surpassing conventional single-object detection models in both detection accuracy and classification efficacy.

The collection of accurate and noise-free data is a crucial part of Internet of Things (IoT)-controlled environments. However, the data collected from various sensors in daily life often suffer from inaccuracies. Additionally, IoT-controlled devices with low-cost sensors lack sufficient hardware resources to employ conventional deep learning models. To overcome this limitation, we propose sensors for daily life (SenDaL), the first framework that utilizes neural networks for calibrating low-cost sensors. SenDaL introduces novel training and inference processes that enable it to achieve accuracy comparable to deep learning models while simultaneously preserving latency and energy consumption similar to linear models. SenDaL is first trained in a bottom-up manner, making decisions based on calibration results from both linear and deep learning models. Once both models are trained, SenDaL makes independent decisions through a top-down inference process, ensuring accuracy and inference speed. Furthermore, SenDaL can select the optimal deep learning model according to the resources of the IoT devices because it is compatible with various deep learning models, such as long short-term memory-based and Transformer-based models. We have verified that SenDaL outperforms existing deep learning models in terms of accuracy, latency, and energy efficiency through experiments conducted in different IoT environments and real-life scenarios.

The global prevalence of childhood and adolescent obesity is a major concern due to its association with chronic diseases and long-term health risks. Artificial intelligence technology has been identified as a potential solution to accurately predict obesity rates and provide personalized feedback to adolescents. This study highlights the importance of early identification and prevention of obesity-related health issues. To develop effective algorithms for the prediction of obesity rates and provide personalized feedback, factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information must be taken into account. Therefore, by collecting health datasets from 321 adolescents who participated in Would You Do It! application, we proposed an adolescent obesity prediction system that provides personalized predictions and assists individuals in making informed health decisions. Our proposed deep learning framework, DeepHealthNet, effectively trains the model using data augmentation techniques, even when daily health data are limited, resulting in improved prediction accuracy (acc: 0.8842). Additionally, the study revealed variations in the prediction of the obesity rate between boys (acc: 0.9320) and girls (acc: 0.9163), allowing the identification of disparities and the determination of the optimal time to provide feedback. Statistical analysis revealed that the performance of the proposed deep learning framework was more statistically significant (p 0.001) compared to the other general models. The proposed system has the potential to effectively address childhood and adolescent obesity.

Artificial intelligence (AI) has transformed medical diagnosis and improved quality of life. But in the field of veterinary medicine has been limited due to training data and obtaining high-quality data. In this study, we propose a framework for diagnosing left atrial enlargement in dogs using AI techniques. Our framework involves generating X-ray image data and utilizing the UNet model for segmentation. The results of our experiments show excellent performance, with a mean dice score of 0.9186 for segmentation. The highest classification accuracy was achieved in trial 1 for normal and overall cases, with 0.9200 and 0.8478, respectively, while trial 2 had the highest abnormal heart classification accuracy of 0.8095. Our findings indicate that generating data and training the model with a certain percentage of the generated data can lead to high classification accuracy. We conclude that the proposed framework has the potential for clinical application in veterinary medicine.

The Internet of Things (IoT) connects a wide range of entities and can be applied to various types of environments. In addition, IoT environments can be dynamically changed at runtime; thus, IoT systems can be deployed in various environments. To support stable operation, IoT systems must adapt to dynamic environmental changes. The self-adaptive software aims to adjust various artifacts or attributes of software to adapt the detected context by itself, and various studies have applied self-adaptive methods in IoT-related research. In this study, we proposed a self-adaptive software framework with master–slave architecture-based finite-state machine modeling. In addition, model checking is applied, which is a formal method to verify IoT systems at runtime, and a cache-based mechanism is applied to reduce the computational time required for verification. To demonstrate the efficiency of the proposed framework, an empirical evaluation was performed with several model-checking tools (RINGA, NuSMV, nuXmv, and CadenceSMV), and the results showed the efficiency of the proposed framework with the cache-based mechanism. In addition, an example application was investigated with smart greenhouse scenarios, and the application was implemented on Android and Arduino. The application was operated in physical environments, and the results showed the practical usability of the proposed framework with verification at runtime.

Currently, software systems are operated in a dynamic and uncertain environment, making it difficult to predict the operating environment. Particularly, the Internet of Things (IoT) interconnects several entities, users, and information resources with services. Therefore, IoT systems can dynamically create various environments at runtime. Consequently, to support the dynamic IoT environment, an efficient verification method is required for IoT systems. Model checking is one of the formal methods for verification of concurrent systems, which is applied in several software fields. However, model checking has a chronic problem (i.e., state explosion) that obstructs the verification at runtime. To overcome this limitation, one possible approach is to abstract the system design as expression forms and then perform verification by solving the expressions. To apply this approach, the abstraction and verification processes need to be performed at a reasonable time. In this article, we focused on developing an efficient model checking method during the IoT system runtime verification. A cache mechanism is proposed that reduces the computational time for abstraction and verification, which was validated through experiments. Additionally, the comparison of other model checking tools (such as RINGA, CadenceSMV, NuSMV, and nuXmv) reveals that the proposed approach is more efficient at runtime.

Duties are segregated within a team by using the role-based access control (RBAC) in the Azure Internet of Things (IoT) framework, and only an appropriate level of access is granted to users to perform specific tasks, depending on a given situation. However, the same authentication and authorization mechanism is used for “sort of user,” which increases the operation overload on the cloud server. Moreover, due to its RBAC nature, the IoT framework is inefficient in handling a dynamic situation where multiple users request similar kinds of resources, by creating several repeated roles. This results in inconsistent and inflexible implementation and the loss of the capability to efficiently address policy management, semantics, redundancy issues in roles, dynamic user handling, work delegation issues, scalability, role explosion, individual rights, and security issues in large organizations. In this article, we designed and presented a novel access control model for a significantly large medical scenario with efficient priority-based authentication mechanisms to address the abovementioned problems associated with the Azure IoT cloud. The proposed model encapsulates the enforcement of priority-based resource access rights across multiple users in a large organization, reduces inefficiency and ineffectuality, and supports individuals with the consistent implementation of policies. We evaluated the benefits of the proposed model by comparing it with existing models and the Azure model, using the healthcare use-case situation. The comparison results show that by incorporating the priority attribute facility in the existing RBAC model, the proposed model classifies the policy mechanism based on priority attributes and proves that the proposed model is capable of handling problems that generally occur when dealing with huge dynamic scenarios in large organizations.

The Internet of Things (IoT) connects several objects within environments that dynamically change, and so requirements may be added and changed at runtime. Therefore, requirements may be satisfied at dynamic change. Self-adaptive software can alter their behavior to satisfy requirements in dynamic environments. In this perspective, the concept of self-adaptive software is suitable for IoT environments. In this study, a self-adaptive framework is proposed for decision making in IoT environments at runtime. The framework includes finite-state machine model designs and game theoretic decision-making methods to extract efficient strategies. The framework is implemented as a prototype, and experiments are performed to evaluate runtime performance. The results demonstrate that the proposed framework can be applied to IoT environments at runtime.

Twitter is a microblogging website, which has different characteristics from any other social networking service (SNS) in that it has one-directional relationships between users with short posts of less than 140 characters. These characteristics make Twitter not only a social network but also a news media. In addition, Twitter posts have been used and analyzed in various fields such as marketing, prediction of presidential elections, and requirement analysis. With an increase in Twitter usage, we need a more effective method to analyze Twitter content. In this paper, we propose a method for content analysis based on the influence of Twitter content. For measuring Twitter influence, we use the number of followers of the content author, retweet count, and currency of time. We perform experiments to compare the proposed method, frequency, numerical statistics, user influence, and sentiment score. The results show that the proposed method is slightly better than the other methods. In addition, we discuss Twitter characteristics and a method for an effective analysis of Twitter content.