This study assessed ICTMT awareness among Indian and Hungarian university students on different continents. He analyzed student attitudes toward ICTMT using differential, inferential, and predictive methods. MLR approaches predicted high Hungarian university student opinions. According to Pearson correlation, technology benefits favorably correlated with student attitude and usability. Exploratory Factor Analysis (EFA) suggested important MLR model elements that predicted student evaluations using educational benefit and usability factors. Thus, Hungarian students believe technology usability and benefits have been greatly affected. Based on ICTMT technology, Indian students' attitude was identified with the LR method. Their attitude and ICTMT advantages correlated positively according to the Pearson Correlation. EFA-PCA recommended all features. The Mann-Whitney U test then compared Indian and Hungarian students' technology use and advantages opinions. The T-test also promotes technology's national benefits. Technology use among Indian and Hungarian students was correlated through Correspondence Analysis (CA). It found a considerable technology benefit for Hungarian pupils but not Indian students. The Chi2, Fisher's Exact (FE), and Cramer's v (CV) tests assessed their viewpoint. The Mann-Whitney U test showed that Hungarian and Indian pupils' opinions differed. Then, Kruskal-Wallis H, Welch's t, and Mann-Whitney U tests compared opinions on technological progress and access. These statistical tests show that Indian and Hungarian students' perspectives differ. The optimistic machine learning models SVM, MLP, and PCA predicted students' nations based on their technology opinions. Another machine learning predictive model identifies ICTMT students' gender and geography. Students, teachers, parents, institutions, and government may benefit from the research. Students in both nations can learn about education technology developments. Management, technical coordinator, and teachers can assess and train pupils' technology awareness.

He developed Student Demographic Identification (SDI) to accurately identify a student's demographic attributes (age, course).  SDI was compared to Random Forest (RF), Logistic Regression (LR), and Radial Support Vector Machine (R–SVM). The suggested technique improves classifier accuracy, F1-score, precision, recall, and Matthews Correlation Coefficient (MCC). He suggested important features to identify students' age, course, and gender. SDI correctly identified the student's age group and course 96% and 97%, respectively. Gradient Boosting (GB) improves LR, R-SVM, and RF gender prediction. RF and GB also identified student genders with 98% accuracy. All three classifiers also correctly recognized the student's area and institution with 99% accuracy. Our SDI algorithm may predict student demographics in real-time surveys.

Using machine learning and statistics, this study examined innovative elements affecting informatics students' satisfaction and happiness. The Random Forest (RF) method accurately predicted student happiness at 88%. Students loved hybrid learning and thought it was better for theory than laboratories. Student's t–test showed that age and course affected hybrid learning safety online (p < 0.05). Students in bachelor's programs (B.Sc.) under 20 were more confident than those in master's programs (M.Sc.) over 21 that hybrid learning is safe during the epidemic. Student satisfaction affects online education safety concerns and prospects. Time and expense utilization with management, University activities and assistance, and hard group work significantly affected student happiness. Future hybrid learning style, Appropriate for theory classrooms and laboratories, and Time and cost utilization with management were important predictors of student satisfaction with all machine learning methodologies. The article suggested hybrid learning challenges, pandemic support, and safety measures for higher education institutions.

A study conducted at Changhua Christian Hospital in China used serum biomarker samples to classify liver disease stages. The researchers used machine learning algorithms like Random Forest, Logistic Regression, XGBoost, and Support Vector Classifier to predict liver risk associated with conditions like Hepatitis, Autoimmune Hepatitis, Alcoholic Liver Disease, and NonAlcoholic Fatty Liver Disease. The study identified three distinct clusters using LSA, with the RF model achieving high accuracy of 0.94±0.06. Key features in the liver disease staging increment were identified, including GPT, Age at Diagnosis, Erythrocyte Sedimentation Rate, and C-reactive protein. The developed model provides valuable decision-making tools for clinicians, enabling early and targeted interventions in liver disease management.

Since the COVID-19 pandemic, teachers and students have started using online and hybrid learning in education. There might be several obstacles to adopting hybrid learning in theory classes or lab practice sessions. Based on student opinions, deciding what is appropriate for theoretical class and lab practice is challenging. We employed machine learning approaches to forecast the hybrid learning mode for theory classes and lab practices. We introduce a framework that utilizes machine learning to automate the identification of hybrid learning for Theory Class and Lab practice (TCLPI). Four machine learning models form the foundation of this framework: Random Forest (RDT), Support Vector Machine (SVN), Logistic Regression (LGR), and Extreme Gradient Boosting (XBT). In the context of Theory Class Identification (TCI), the SVN achieves a maximum test accuracy of 0.93, whereas the LGR achieves a minimum accuracy of 0.90. On the other hand, the Lab Practice Identification (LPI), XBT, RDT, and SVN achieved a test accuracy of 0.80. The outcome of trained algorithms is assessed using the Shapley Additive Explanation (SHAP), an explainable Artificial intelligence (AI) approach. This research found that student-teacher interaction decreased during lab practice, which is crucial. Internet disconnections, a lack of support during technological malfunctions, and the likelihood of cheating in exams without monitoring are also issues. We also found that students were accepting of hybrid learning for theory classes. Each model’s intrinsic feature relevance and SHAP values helped prove this. Research shows that hybrid learning works more for theory classes; it is less needed for lab practice for students.

The security of 3-D images is an important research problem that has to be resolved. Due to the more complex structure of 3-D images, the encryption of these images is quite different from 1-D and 2-D images. A threestage novel image encryption technique based on chaotic maps is presented in this paper in which a 3-D image is firstly converted to a similar image format as that of 2-D images before encryption. The initial conditions of the chaotic system are generated by using the SHA-256 function on the coordinate matrix of the plaintext. Initially, a logistic map is utilized to scramble and add random points to the coordinate values of vertices of a 3-D image. The coordinate values are then confused and diffused in the second stage by using three sequences generated through the logistic-dynamic coupled logistic map lattice (LDCML) model. This stage also involves splitting of floating point data into integer and fractional parts. The integer part is diffused whereas the fractional part is scrambled during this process. In the third stage, the confusion is performed by using a tent map among the coordinate points. This process enhances the robustness, integrity, and confidentiality of 3-D images and ensures protection against unauthorized access. The proposed encryption procedure achieves values of correlation close to zero along x,y,z- directions, NPCR of 100%, UACI of 33.37%, and entropy value of 7.9993 which demonstrates its robust security. The time analysis shows that our technique improves efficiency and lowers computational costs by processing data in a lesser time. The security and statistical analysis concludes that the proposed encryption algorithm can withstand various conventional attacks.

Background and Objective: Early detection of eye diseases, especially cataracts, is essential for preventing vision impairment. Accurate and cost-effective cataract diagnosis often requires advanced methods. This study proposes novel deep learning models that integrate global and local attention mechanisms into MobileNet and InceptionV3 architectures to improve cataract detection from fundus images. Methods: Two deep learning models, Global–Local Attention Augmented MobileNet (GLAAM) and Global– Local Attention Augmented InceptionV3 (GLAAI), were developed to enhance the analysis of fundus images. The models incorporate a combined attention mechanism to effectively capture deteriorated regions in retinal images. Data augmentation techniques were employed to prevent overfitting during training and testing on two cataract datasets. Additionally, Grad-CAM visualizations were used to increase interpretability by highlighting key regions influencing predictions. Results: The GLAAM model achieved a balanced accuracy of 97.08%, an average precision of 97.11%, and an F1-score of 97.12% on the retinal dataset. Grad-CAM visualizations confirmed the models’ ability to identify crucial cataract-related regions in fundus images. Conclusion: This study demonstrates a significant advancement in cataract diagnosis using deep learning, with GLAAM and GLAAI models exhibiting strong diagnostic performance. These models have the potential to enhance diagnostic tools and improve patient care by offering a cost-effective and accurate solution for cataract detection, suitable for integration into clinical settings.

Image ciphering techniques usually transform a given plain image data into a cipher image data resembling noise, serving as an indicator of the presence of secret image data. However, the transmission of such noise-like images could draw attention, thereby attracting the attackers and may face several possible attacks. This paper presents an approach for generating a visually meaningful image encryption (VMIE) scheme that combines three layers of security protection: encryption, digital signature, and steganography. The present scheme is dedicated to achieving a balanced performance in robustness, security and operational efficiency. First, the original image is partially encrypted by using the RSA cryptosystem and modified Hénon map (MHM). In the second stage, a digital signature is generated for the partially encrypted image by employing a hash function and the RSA cryptosystem. The obtained digital signature is appended to the partially encrypted image produced after implementing the zigzag confusion in the above partially encrypted image. Further, to achieve better confusion and diffusion, the partially encrypted image containing a digital signature undergoes through the application of 3D Arnold cat map (ARno times), to produce the secret encrypted image (Sr5). To ensure the security and robustness of the proposed technique against various classical attacks, the hash value obtained from the SHA-256 hash function and carrier images is utilized to generate the initial conditions Mh10 and Mh20 for modified Hénon map, and initial position Zip = (zrow, zwcol) for zigzag confusion. In the proposed algorithm, the digital signature is utilized for both purposes to verify the sender’s authenticity and to enhance the encryption quality. The carrier image undergoes lifting wavelet transformation, and its high-frequency components are utilized in the embedding process through a permuted pattern of MHM, resulting in a visually meaningful encrypted image. The proposed scheme achieves efficient visual encryption with minimal distortion and ensures lossless image quality upon decryption (infinite PSNR), balancing high level of security along with a good computational efficiency.

Background & Objective

Liver disease remains a significant global health burden, often progressing silently until advanced stages such as cirrhosis or hepatic failure. Early detection is essential but remains hindered by the limitations of conventional diagnostics. This study presents LivXAI-Net, an explainable artificial intelligence (XAI) framework integrated with Internet of Things (IoT) biosensors, designed and evaluated in a simulated real-time setting using a historical dataset.

Methods:

LivXAI-Net simulates continuous data acquisition from wearable biosensors — including sweat, platelet, and prothrombin sensors — and processes this data using machine learning (ML) models trained on the Mayo Clinic primary biliary cirrhosis (PBC) dataset (n=

424, 1974–1984). Random Forest (RF) and XGBoost(XGB) classifiers were deployed with SHAP and Permutation Feature Importance (PFI) to enhance interpretability. A mobile application, Hepatic Health Tracker, delivers real-time risk predictions, supported by a secure data pipeline using TLS 1.3 and AES-256 encryption.

Results:

RF and XGB achieved accuracies of 84% and 82% respectively under 20-fold cross-validation. Key biomarkers — albumin, cholesterol, and triglycerides — were consistently identified by SHAP as influential in classification. The system achieved a total latency of 0.85 s in a simulated 5G environment, supporting near-instantaneous alert delivery via the mobile interface.

Conclusion:

LivXAI-Net combines interpretable ML with real-time biosensor data to enable proactive liver disease management. While currently validated using historical data in a simulated environment, future work will involve deployment with live sensor input and clinical trials to validate utility and generalizability in real-world settings.

Recent technological advancements have facilitated the widespread integration of robots across various sectors, including healthcare, defense, and industries. The Internet of Things (IoT) based robotic devices, equipped with advanced sensors and imaging capabilities, are crucial for various tasks such as environmental monitoring, autonomous navigation, and real-time surveillance. For real-time surveillance, robotic devices contribute to effective, safe, and reliable communication. However, the communication, in real-time environment, faces several challenges such as security and resource optimization. Due to limited computational resources, IoT-based systems need lightweight security solutions for communication efficiency and secure data handling. To enhance communication efficiency with secure data handling, this study introduces a Lightweight Surveillance Framework for Robotic Systems (LSFRS). The LSFRS optimizes data transmission by leveraging the Structural Similarity Index to eliminate redundancy and improving resource utilization. For secure communication, lightweight encryption methods: Tiny Encryption Algorithm (TEA), eXtended TEA (XTEA), Caesar Cipher, and Exclusive or (XOR), are employed. The proposed framework is evaluated for encryption performance, computational efficiency, and suitability for real-time surveillance. Comparative analysis demonstrates that the proposed framework is competitive against existing techniques.