Background: Accurate classification of brain tumors in medical images is vital for effective diagnosis and treatment planning, which improves the patient’s survival rate. In this paper, we investigate the application of Convolutional Neural Networks (CNN) as a powerful tool for enhancing diagnostic accuracy using a Magnetic Resonance Imaging (MRI) dataset. Method: This study investigates the application of CNNs for brain tumor classification using a dataset of Magnetic Resonance Imaging (MRI) with a resolution of 200 × 200 × 1. The dataset is pre-processed and categorized into three types of tumors: Glioma, Meningioma, and Pituitary. The CNN models, including the Classic layer architecture and the ResNet50 architecture, are trained and evaluated using an 80:20 training-testing split. Results: The results reveal that both architectures accurately classify brain tumors. Classic layer architecture achieves an accuracy of 94.55%, while the ResNet50 architecture surpasses it with an accuracy of 99.88%. Compared to previous studies and 99.34%, our approach offers higher precision and reliability, demonstrating the effectiveness of ResNet50 in capturing complex features. Conclusions: The study concludes that CNNs, particularly the ResNet50 architecture, exhibit effectiveness in classifying brain tumors and hold significant potential in aiding medical professionals in accurate diagnosis and treatment planning. These advancements aim to further enhance the performance and practicality of CNN-based brain tumor classification systems, ultimately benefiting healthcare professionals and patients. For future research, exploring transfer learning techniques could be beneficial. By leveraging pre-trained models on large-scale datasets, researchers can utilize knowledge from other domains to improve brain tumor classification tasks, particularly in scenarios with limited annotated data.

Artificial intelligence (AI)-powered mental health chatbots have evolved quickly as scalable means for psychological support, bringing novel solutions through natural language processing (NLP), mobile accessibility, and generative AI. This systematic literature review (SLR), following PRISMA 2020 guidelines, collates evidence from 25 published, peer-reviewed studies between 2020 and 2025 and reviews therapeutic techniques, cultural adaptation, technical design, system assessment, and ethics. Studies were extracted from seven academic databases, screened against specific inclusion criteria, and thematically analyzed. Cognitive behavioral therapy (CBT) was the most common therapeutic model, featured in 15 systems, frequently being used jointly with journaling, mindfulness, and behavioral activation, followed by emotion-based approaches, which were featured in seven systems. Innovative techniques like GPT-based emotional processing, multimodal interaction (e.g., AR/VR), and LSTM-SVM classification models (greater than 94% accuracy) showed increased conversation flexibility but missed long-term clinical validation. Cultural adaptability was varied, and effective localization was seen in systems like XiaoE, okBot, and Luda Lee, while Western-oriented systems had restricted contextual adaptability. Accessibility and inclusivity are still major challenges, especially within low-resource settings, since digital literacy, support for multiple languages, and infrastructure deficits are still challenges. Ethical aspects—data privacy, explainability, and crisis plans—were under-evidenced for most deployments. This review is different from previous ones since it focuses on cultural adaptability, ethics, and hybrid public health incorporation and proposes a comprehensive approach for deploying AI mental health chatbots safely, effectively, and inclusively. Central to this review, symmetry is emphasized as a fundamental idea incorporated into frameworks for cultural adaptation, decision-making processes, and therapeutic structures. In particular, symmetry ensures equal cultural responsiveness, balanced user–chatbot interactions, and ethically aligned AI systems, all of which enhance the efficacy and dependability of mental health services. Recognizing these benefits, the review further underscores the necessity for more rigorous academic research into the development, deployment, and evaluation of mental health chatbots and apps, particularly to address cultural sensitivity, ethical accountability, and long-term clinical outcomes.

Personal health records can be considered as a noticeable advancement in consumer health informatics. PHRs have been adopted in many nations, whereas other countries are planning to adopt this technology. Understanding the user acceptance of PHRs by organizations and individuals is a critical issue. Many researches discussed the acceptance and adoption of PHRs; few studies were experimental. The main objective of this study is reviewing the related studies that have been made in order to investigate the user acceptance of PHRs through reviewing the theories that have been used, the main factors involved, and any missing aspects to be applied in this field. The review's major components are: topic, location of the study, sample size, statistical tools, models or theories, goals, and findings of the study.

Cryptography is a technique that is widely used to secure data transactions, this technique has also been widely used to secure digital images. There are several cryptographic techniques that are widely used in digital imagery, such as Arnold Transform, Rivest Code 4 (RC4) and One-time pad (OTP). This research aims to compare the performance of these three techniques on digital images. Performance of each encryption method is measured using entropy, SSIM, NPCR, UACI, computation time and histogram analysis. While the decrypted image is measured by the correlation coefficient and computation time using tic toc function. Based on the test results can be concluded that Arnold transforms algorithm only do a scramble on the image based on a certain iteration, so that the entropy and histogram values remain the same as the original image, only visually the image looks random and meaningless. RC4 has an advantage on entropy value and should not require a very long key, so it does not have to be a special memory to store the key. While OTP has advantages in the fastest computing time, and also superior values in NPCR, UACI, and SSIM. A random key of the same size as the original image makes encryption hard to decrypt. Although the size of the key to making the algorithm must provide a larger space for key storage.

For the early identification of diabetes, a chronic illness that affects millions of people globally, artificial intelligence (AI) shows enormous promise. AI algorithms can recognize diabetes signs and give patients and healthcare providers early warnings by examining a variety of data sources, such as medical records, patient histories, and lifestyle factors. AI's capacity to evaluate sizable and intricate datasets is one of its main advantages in the diagnosis of diabetes. AI is capable of taking into account a broad range of variables that could lead to diabetes, such as age, BMI, food preferences, physical activity, and genetic predisposition. Artificial intelligence (AI) systems can recognize early indicators of diabetes and forecast the risk of developing the condition by analysing patterns and relationships among these variables. AI can also be applied to customize diabetic care and screening. Healthcare providers can improve patient outcomes and lessen the burden of disease by customizing suggestions and treatment options for each patient. AI can assess a patient's risk of diabetes by looking into their lifestyle choices and medical history. Healthcare providers can focus treatments and preventative actions to lower the chance of illness onset by identifying high-risk individuals can identify early indicators of diabetes by analysing blood glucose levels, demographic data, and lifestyle factors. Because of this, medical practitioners may be able to intervene early on, when the illness is most amenable to treatment systems that can monitor blood glucose levels and examine patient histories to create customized diabetic treatment programs. This can involve individualized prescription schedules, workout programs, and food advice. All things considered, AI has the power to completely transform the way people with diabetes are managed by giving patients and healthcare providers individualized data-driven insights. The use of AI in clinical practice is fraught with difficulties, such as privacy issues and a dearth of standardized data, yet the advantages in identifying and treating diabetes are substantial.