The skin is the outermost part of the human body that is often exposed to the environment, so it is easy to experience disease disorders. Some of the skin diseases that are often contracted in humans are ulcers, herpes, and warts. Untreated skin diseases will be very annoying because of the sensation of itching so it can cause irritation and inflammation. The ability to classify skin diseases using technology is one solution. This study uses the K-Nearest Neighbour (KNN) method to detect images of skin diseases. KNN is one of the machine learning methods with a calculation method based on the proximity of k. KNN was chosen because it is fast and has high-accuracy results. The results of the research that has been carried out have obtained results of accuracy of 63%, precision of 63%, recall of 63%, and F1 Score of 63%. From the results of the study, it can be concluded that disease detection using KNN has been successfully applied and can be used in classification.

Human health is the main focus of clinical medicine, especially in understanding internal diseases involving the body's organs. Identifying and predicting disease at an early stage is essential to prevent the development of more severe disease. These challenges encourage using the latest technologies, especially machine learning techniques. This technology is used to ensure accurate disease predictions. The results of the research identified various types of internal diseases, including heart, kidney, lung and liver cancer, and highlighted the associated symptoms and risk factors. Several algorithms are used to classify internal diseases, including the classification of heart disease. The logistic regression algorithm is the most efficient, with accuracy results of 88.52%. Meanwhile, CHIRP kidney disease classification provides the most efficient results with an accuracy of 99.75%. MobileLungNetV2 has an accuracy of 96.97% for lung disease classification, and classification for liver disease produces the highest accuracy in logistic regression at 72.50%. Machine learning in disease prediction significantly contributes, especially in increasing accuracy and efficiency in diagnosis and risk prediction. Despite significant progress, challenges such as dataset size, data quality, and model validation need to be addressed to maximise the potential of this technology in clinical practice.

This study looks into the effects of modern and traditional coffeeshops on the mental health of customers in the Saudi Arabian province of Al Qassim, with a focus on the cities of Buraydah and Unaizah. The proliferation of diverse coffeeshop kinds has resulted in the emergence of each giving a distinct experience and atmosphere. Understanding the impact of different coffee shop types on mental well-being is critical for the creation of long-lasting and prosperous coffee communities in Al Qassim. The study addresses five major concerns: the impact of coffee shops on consumers' sense of autonomy and empowerment, the impact of environmental stimuli on psychological well-being, the role of quality and satisfaction in shaping coffee shop experiences, strategies for bridging the gap between modern and traditional coffeeshops. To gain insights, a thorough research technique was used, which included a qualitative literature review, talks, and observations. The findings emphasize the considerable impact of modern and traditional coffeeshops on mental health, underlining the necessity of collaboration and sustainability in cultivating an inclusive coffee culture that improves the well-being of the Al Qassim society.

Diabetic foot ulcer (DFU) image segmentation is still an interesting concern of researchers. Various new deep learning-based methods have been proposed to handle this image segmentation problem. Some research problems that are still faced by many researchers are dataset problems that are considered limited and need further clinical trials. The challenges of data problems include heterogeneity and image quality variations in the shape of skin lesions and subjectivity when annotating. The evaluation results from previous studies also show a considerable difference where there are still low accuracy results, but also too high accuracy is still found so that it is considered to have the potential for overfitting. As a result of the review of various related studies, there is an interesting potential of applying fuzzy logic to the U-Net architecture. This architecture has become very popular because it is widely used in medical image segmentation. The application of fuzzy logic can be applied to the U-Net architecture such as encoder, decoder, skip connection to adjust various U-Net parameters.

The rapid development of technology has given birth to various sophisticated innovations, one of which is robot technology. In the context of today's evolving nursing world, attention to nurses' ability to deal with technological developments, including the use of robots, is becoming increasingly important to consider the steps that need to be taken in order to achieve and maintain patient health with the help of robots as intelligent tools. The aim of this study was to thoroughly assess the impact of the use of robotic technology in nursing care through a systematic review of relevant literature. This type of research is a systematic literature review. The sample in this study was 17 articles using the prism diagram method. Article retrieval was carried out using the Sage Journal search engine, PubMed, ProQuest, Google Scholar. Based on the results obtained positive and negative impacts of the use of robotic technology in nursing services. Positive impacts include aspects of mobilization, psychology, independent needs, nutritional fulfillment, safety, logistical needs, time management. However, the main challenges include economic, psychological, management, collaboration, social values. These studies meet the five assessments of service quality; Physical appearance, reliability, responsiveness, assurance, empathy. Apart from these five assessments, there are economic, management, collaboration challenges that need to be considered

Viruses and bacteria continue to evolve alongside humans. Viruses are spreading too fast and causing a huge loss of life in the world. Viruses play an important role as dangerous pathogens that continue to spread various infectious diseases. Metegenomics is the application of large sequencing technology to genetic material obtained directly from one or more environmental samples, resulting in at least 50Mb random samples and multiple long sequences. It is important to identify the origin of the virus to prevent the spread of outbreaks. Understanding the biology of these viruses and how they affect their ecosystems depends on knowing which host they infect. We can use metagenomic features derived from the viral genome to determine the type of virus host. The activity of predicting virus hosts has traditionally taken a lot of time and effort in the process. Technology can be one of the solutions that can be used to predict virus host types. One of the technologies that can be used is machine learning. We chose one of the machine learning algorithms, SVM, to predict viral hosts with metagenomics features, namely genome size, GC% and number of CDS from viral genomes derived from 7326 viral genomes. The SVM model was further optimised with GS and K-CV methods. This optimisation resulted in an increase in the accuracy value of the model when predicting virus hosts from 80% to 84%.

This study conducts a bibliometric analysis and systematic review to examine research trends in the application of knowledge distillation for medical image segmentation. A total of 806 studies from 343 distinct sources, published between 2019 and 2023, were analyzed using Publish or Perish and VOSviewer, with data retrieved from Scopus and Google Scholar. The findings indicate a rising trend in publications indexed in Scopus, whereas a decline was observed in Google Scholar. Citation analysis revealed that the United States and China emerged as the leading contributors in terms of both publication volume and citation impact. Previous research predominantly focused on optimizing knowledge distillation techniques and their implementation in resource-constrained devices. Keyword analysis demonstrated that medical image segmentation appeared most frequently with 144 occurrences, followed by medical imaging with 110 occurrences. This study highlights emerging research opportunities, particularly in leveraging knowledge distillation for U-Net architectures with large-scale datasets and integrating transformer models to enhance medical image segmentation performance

Foodborne diseases remain a significant global public health concern, affecting millions annually and causing substantial economic losses. Traditional microbiological methods for pathogen detection, such as culture-based identification and polymerase chain reaction, are often time-consuming and lack sensitivity. The integration of bioinformatics and high-throughput sequencing technologies, including next-generation sequencing and metagenomics, has revolutionized foodborne pathogen detection by enabling rapid, accurate, and culture-independent identification. Machine learning and artificial intelligence further enhance food safety monitoring through predictive modeling and risk assessment, facilitating early outbreak detection and improved contamination control. Whole genome sequencing has emerged as a gold standard for public health surveillance, allowing for precise pathogen characterization and antimicrobial resistance tracking. Data-sharing networks, such as GenomeTrakr and PulseNet, have strengthened global collaboration, enhancing real-time pathogen monitoring. However, challenges persist in data integration, technical expertise, and infrastructure development, which hinder the widespread adoption of these technologies. Addressing these barriers requires standardized protocols, AI-driven predictive models, and interdisciplinary collaboration between public health, industry, and academia. As bioinformatics continues to evolve, its role in pathogen surveillance, outbreak prevention, and food safety management will become increasingly vital. Advancements in bioinformatics tools and AI-driven approaches will ensure a more efficient, data-driven, and globally coordinated response to foodborne disease threats

Medical image segmentation plays a crucial role in computer-aided diagnosis by enabling precise identification of anatomical and pathological structures. While deep learning models have significantly improved segmentation accuracy, their high computational complexity limits deployment in resource-constrained environments, such as mobile healthcare and edge computing. Knowledge Distillation (KD) has emerged as an effective model compression technique, allowing a lightweight student model to inherit knowledge from a complex teacher model while maintaining high segmentation performance. This review systematically examines key KD techniques, including Response-Based, Feature-Based, and Relation-Based Distillation, and analyzes their advantages and limitations. Major challenges in KD, such as boundary preservation, domain generalization, and computational trade-offs, are explored in the context of lightweight model development. Additionally, emerging trends, including the integration of KD with Transformers, Federated Learning, and Self-Supervised Learning, are discussed to highlight future directions in efficient medical image segmentation. By providing a comprehensive analysis of KD for lightweight segmentation models, this review aims to guide the development of deep learning solutions that balance accuracy, efficiency, and real-world applicability in medical imaging

This systematic literature review explores the use of blockchain technology to improve the security of communication between IoT devices in the healthcare sector. The integration of IoT in healthcare has revolutionized patient data management but introduced security challenges. Blockchain, as a decentralized and immutable ledger, offers a potential solution by providing a secure method of data storage and transmission. This review analyzed 62 relevant studies published in the last five years using the PRISMA methodology. The main contributions of blockchain include improved data security, privacy, and data integrity, with decentralization and cryptographic techniques ensuring patient data remains secure and accessible only to authorized entities. Challenges of blockchain implementation include interoperability, data storage efficiency, and the need for strong cryptographic algorithms. Proposed solutions include the development of a specialized blockchain framework for healthcare, the integration of advanced encryption methods, and the use of distributed ledger technology to manage electronic medical records. Further research is needed to develop more efficient and secure blockchain solutions in healthcare applications, including improved interoperability, encryption algorithms, and real-world case studies. Although challenges remain, blockchain has great potential to improve the communication security of IoT devices in the healthcare sector.