With the rapid advancements of the internet of things (IoT), several applications have evolved with completely dissimilar structures and requirements. However, the fifth generation of mobile cellular networks (5G) is unable to successfully support the dissimilar structures and requirements. The sixth generation of mobile cellular networks (6G) is likely to enable new and unidentified applications with varying requirements. Therefore, 6G not only provides 10 to 100 times the speed of 5G, but 6G can also provide dynamic services for advanced IoT applications. However, providing security to 6G networks is still a significant problem. Therefore, in this paper, a hybrid image encryption technique is proposed to secure multimedia data communication over 6G networks. Initially, multimedia data are encrypted by using the proposed model. Thereafter, the encrypted data are then transferred over the 6G networks. Extensive experiments are conducted by using various attacks and security measures. A comparative analysis reveals that the proposed model achieves remarkably good performance as compared to the existing encryption techniques.

With the advancements in e-healthcare services, it is possible to provide remote medical services to patients and swifter first aid. Medical images play an essential role in e-healthcare applications for providing quick and better remote diagnosis and treatment to patients. Medical images generally comprise secret details about the patients and are therefore prone to various security threats during their transmission over public networks. Thus, it is required to secure these images prior to their communication over public networks. But due to distinctive properties of medical images, like higher correlation and redundancy among the pixels, and larger size, it is required to design an efficient encryption model to resist against various security threats. In this paper, an efficient encryption model for medical images is proposed. To obtain the secret keys, six-dimensional hyperchaotic map (SDHM) is proposed. Firstly, plain medical image is divided into three channels such as red, green, and blue. Secret keys are used to diffuse these channels. Lastly, encrypted channels are concatenated and final encrypted medical image is obtained. Extensive experiments are drawn by considering the benchmark medical images. Also, comparisons are performed among the proposed SDHM and competitive techniques by considering various performance metrics. Comparative analysis reveals that the proposed SDHM achieves remarkably good performance than the existing encryption models.

Potatoes are an important crop heavily consumed by Indian food products. It is produced on a massive scale, with China, India, Russia, Poland, and the USA being the main producers. Numerous leaf diseases harm the crop during its production. A typical Indian farmer lacks the tools necessary to detect Leaf Disease before damage is done. On a dataset of potato leaf images retrieved from Kaggle, we employed the EfficientNetB0 of Deep Learning to address this problem. This model uses width scaling and resolution scaling apart from depth scaling to perform the classification. Our work mainly focuses on the diseases Early Blight and Late Blight, two serious potato diseases. Early blight Spots start off as tiny, dry, dark, and papery specks that develop into brown to black, circular to oval-shaped regions. Veins that round the spots frequently give them an angular appearance. Late blight syntoms appear as small, light to dark green and round to irregularly shaped. Water-soaked patches are the first signs of late blight. The Data Collection has 2152 pictures in total, 2000 of which are diseased and 152 of which are healthy. The deep learning model provides a testing accuracy of 99.05%, which is higher than several widely used techniques available to provide farmers with knowledge about correct diseases well in time.

Mental health disorders, including anxiety, depres-sion, and stress, profoundly impact individuals’ well-being and necessitate effective early detection for timely intervention. This research investigates the predictive capabilities of machine learning algorithms in assessing anxiety, depression, and stress levels based on questionnaire-derived scores. Utilizing a dataset comprising self-reported scores obtained through a tailored questionnaire designed for mental health assessment, we delve into the application of Decision Trees, Naive Bayes, Support Vector Machines (SVM), and Random Forests for prediction. Data preprocessing involved comprehensive cleaning, encoding categorical variables, and careful feature selection, ensuring the relevance of features in the predictive models. Each algorithm un-derwent individual implementation, wherein we scrutinized their performances in predicting mental health conditions. Evaluation metrics such as accuracy, precision, and recall were employed to assess the models’ proficiency in predicting anxiety, depression, and stress levels. The findings underscore the potential of machine learning in accurately predicting mental health conditions based on questionnaire responses, offering insights into personalized interventions and early detection systems. This study contributes to advancing the understanding of machine learning applications in mental health assessment, highlighting avenues for impactful interventions in mental health care.

Brain tumours pose a substantial global health issue, emphasising the criticality of timely and precise identification to ensure optimal treatment outcomes. This research introduces an innovative methodology for the identification of brain tumours by employing a fusion of Convolutional Neural Networks (CNNs) with threshold segmentation techniques. The objective of the suggested model is to improve the precision and effectiveness of brain tumour identification in medical imaging, namely Magnetic Resonance Imaging (MRI) scans. Fast and precise diagnosis is necessary in the medical profession for effective treatment, but current technologies lack this capability. For successful therapy, it is therefore necessary to develop an effective diagnosis application. Global threshold segmentation for pre-processing is used in this study. Image capture and de-noising were completed in the first stage, while classification and regression were completed in the second stage using ML approaches. A computer-aided automated identification method is the computational technique used in this study. This investigation uses one hundred twenty (120) brain scans from a real-time MRI brain database, of them 15 normal and 105 abnormal. According to performance metrics, the accuracy of training and testing pictures was 99.46%. Comparing this method to recently published methods, it is determined that LR-ML with the threshold segmentation has a rapid, precise brain diagnostic system.