Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the end of 2050. The estimated cost of Alzheimer’s and other related ailments is USD321 billion in 2022 and can rise above USD1 trillion by the end of 2050. Therefore, the early prediction of such diseases using computer-aided systems is a topic of considerable interest and substantial study among scholars. The major objective is to develop a comprehensive framework for the earliest onset and categorization of different phases of Alzheimer’s. Methods: Experimental work of this novel approach is performed by implementing neural networks (CNN) on MRI image datasets. Five classes of Alzheimer’s disease subjects are multi-classified. We used the transfer learning determinant to reap the benefits of pre-trained health data classification models such as the MobileNet. Results: For the evaluation and comparison of the proposed model, various performance metrics are used. The test results reveal that the CNN architectures method has the following characteristics: appropriate simple structures that mitigate computational burden, memory usage, and overfitting, as well as offering maintainable time. The MobileNet pre-trained model has been fine-tuned and has achieved 96.6 percent accuracy for multi-class AD stage classifications. Other models, such as VGG16 and ResNet50 models, are applied tothe same dataset whileconducting this research, and it is revealed that this model yields better results than other models. Conclusion: The study develops a novel framework for the identification of different AD stages. The main advantage of this novel approach is the creation of lightweight neural networks. MobileNet model is mostly used for mobile applications and was rarely used for medical image analysis; hence, we implemented this model for disease detection andyieldedbetter results than existing models.

A brain tumor can have an impact on the symmetry of a person’s face or head, depending on its location and size. If a brain tumor is located in an area that affects the muscles responsible for facial symmetry, it can cause asymmetry. However, not all brain tumors cause asymmetry. Some tumors may be located in areas that do not affect facial symmetry or head shape. Additionally, the asymmetry caused by a brain tumor may be subtle and not easily noticeable, especially in the early stages of the condition. Brain tumor classification using deep learning involves using artificial neural networks to analyze medical images of the brain and classify them as either benign (not cancerous) or malignant (cancerous). In the field of medical imaging, Convolutional Neural Networks (CNN) have been used for tasks such as the classification of brain tumors. These models can then be used to assist in the diagnosis of brain tumors in new cases. Brain tissues can be analyzed using magnetic resonance imaging (MRI). By misdiagnosing forms of brain tumors, patients’ chances of survival will be significantly lowered. Checking the patient’s MRI scans is a common way to detect existing brain tumors. This approach takes a long time and is prone to human mistakes when dealing with large amounts of data and various kinds of brain tumors. In our proposed research, Convolutional Neural Network (CNN) models were trained to detect the three most prevalent forms of brain tumors, i.e., Glioma, Meningioma, and Pituitary; they were optimized using Aquila Optimizer (AQO), which was used for the initial population generation and modification for the selected dataset, dividing it into 80% for the training set and 20% for the testing set. We used the VGG-16, VGG-19, and Inception-V3 architectures with AQO optimizer for the training and validation of the brain tumor dataset and to obtain the best accuracy of 98.95% for the VGG-19 model.

The advancements made by information technology have redefined the concept, scope, and significance of communication. The barriers in the communication process have been wiped out by the recent advances in information and communication technology(ICT) backed by high-speed data connectivity. People are free to communicate without bothering about physical borders distancing them from one another. Information and communication technology has diversified its dynamism by creating an e-environment, where people exploit the power of technology and communication to deliver many services. This research used the conceptual framework for ICT-enabled learning management systems and described their dimensions and scope in ICT-enabled education. The ubiquity of ICT has revamped the education industry worldwide by introducing new approaches, tools, and techniques to modernize education. The widespread popularity of ICT has forced educational establishments to endorse this to update the academia to leverage its bounders and enhance productivity to yield productive outcomes at different levels of education. This paper describes different ICT approaches and investigates the importance, influence, and impact of ICT-enabled technologies on various educational practices to achieve productive educational outcomes. This research investigates the role of ICT in teaching and learning at different levels of education, explores various modulates and their influence on the overall development of educational activities, and identifies the research gaps that are bridged to achieve the primary aim of ICT and education. This research extended its ICT projections and scope to overcome the challenges emerging from pandemic circumstances and design and develop an online platform in proper consultation with market demand to make students more job-oriented or skill-oriented. This paper describes different ICT approaches adopted by various educational institutions across the globe to modernize student−teacher interaction. This paper further investigates the influence and impact of ICT-enabled technologies on various educational practices that are prerequisites for achieving productive educational outcomes.

Introduction. Heart disease is emerging as the single most critical cause of death worldwide and is one of the costliest chronic conditions. Purpose. Stimulated by the increasing heart disease mortality rate incidents, an effective, low-cost, and reliable heart disease risk evaluation model is developed using significant non-invasive risk attributes. The significant non-invasive risk attributes like (age, systolic BP, diastolic BP, BMI, hereditary factor, smoking, alcohol, and physical inactivity) are identified by the help of medical domain experts, and their reliability in heart disease prediction is investigated through different feature selection techniques. Methodology. The enhancements of applying specific investigated techniques like random forest, Naïve Bayes, decision tree, support vector machine, and K nearest neighbor to the risk factors are tested. The heart disease risk assessment model is developed using the Jupyter Notebook web application, and its performance is tested not only through medical domain measures but also through the model performance measures. Findings. To evaluate heart disease risk evaluation model, we calculated measures of discrimination like error rate, AUROC, sensitivity, specificity, accuracy, precision, and so on. Experimental results show that the random forest heart disease risk evaluation model outperforms other existing risk models with admirable predictive accuracy and minimum misclassification rate. Originality. The heart disease risk evaluation model is developed based on novel non-invasive heart disease dataset, which consists of 5776 records. This dataset is collected from different heterogeneous data sources of Kashmir (India) through quantitative data collection methods. Research Implications. The risk model is applicable where people lack the facilities of integrated primary medical care technologies for untimely heart disease risk prediction. Future Work. To investigate deep learning and study the significance of other controlled attributes on different age and sex groups in the risk estimation of heart disease.

Heart disease is a severe disorder, which inflicts an adverse burden on all societies and leads to prolonged suffering and disability. We developed a risk evaluation model based on visible low-cost significant noninvasive attributes using hyperparameter optimization of machine learning techniques. The multiple set of risk attributes is selected and ranked by the recursive feature elimination technique. The assigned rank and value to each attribute are validated and approved by the choice of medical domain experts. The enhancements of applying specific optimized techniques like decision tree, k-nearest neighbor, random forest, and support vector machine to the risk attributes are tested. Experimental results show that the optimized random forest risk model outperforms other models with the highest sensitivity, specificity, precision, accuracy, AUROC score, and minimum misclassification rate. We simulate the results with the prevailing research; they show that it can do better than the existing risk assessment models with exceptional predictive accuracy. The model is applicable in rural areas where people lack an adequate supply of primary healthcare services and encounter barriers to benefit from integrated elementary healthcare advances for initial prediction. Although this research develops a low-cost risk evaluation model, additional research is needed to understand newly identified discoveries about the disease.

This paper presents a structured review of the applications of Artificial Intelligence (AI) in agribusiness, emphasizing its transformative impact on farming practices. By integrating AI technologies such as machine learning, robotics, and data analytics, AI enhances productivity, sustainability, and profitability in agriculture. The paper explores key AI-driven advancements in precision agriculture, resource management, and supply chain optimization, which allow for real-time monitoring and informed decision-making. Additionally, the research discusses the ethical challenges and barriers to AI adoption, particularly in smallholder farming and developing economies. It also identifies emerging trends, such as the integration of AI with blockchain and biotechnology, to further optimize agricultural processes. The paper concludes with recommendations for advancing AI adoption, addressing data privacy concerns, and fostering inclusive, sustainable farming practices to ensure long-term resilience and food security in the agricultural sector.