Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The K‐means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.

Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.

The purpose of this study is to look into the components that affect the behavioral intention of Jordanians to use Virtual Reality Technology in the Learning Environment (VRTLE). For the educational and entertainment industries, virtual reality presents both obstacles and opportunities. The goal of this study is to determine the factors that influence VRTLE acceptance and to propose how such technology may be integrated into the educational setting of students and universities. A total of 60 students from private universities took part in the research. Among the participants, 63.3 were females and 36.7 were males. The modified UTAUT model used in this study only looked at the effects of four independent variables and two external factors, namely, acceptability (ACC) and usability (USA). Acceptability and usability have a substantial impact on student acceptance of VRTLE, according to the findings. Furthermore, as moderators, there is a major effect on both Awareness and Experience. This paper contributes significantly to the UTUAT model. It also tackles educational concerns about the transition to a virtual reality learning environment. As a result, it would be useful to look into other factors as well. As a result, a comprehensive empirical study should be designed to allow for the assessment of the effects of other variables as well.

In recent years, agricultural image processing research has been a key emphasis. Image processing techniques are used by computers to analyze images. New advancements in image capture and data processing have simplified the resolution of a wide range of agricultural concerns. Crop disease classification and identification are crucial for the agricultural industry’s technical and commercial well-being. In agriculture, image processing begins with a digital color picture of a diseased leaf. Plant health and disease detection must be monitored on a regular basis in property agriculture. Plant diseases have had a tremendous impact on civilization and the Earth as a whole. Extensions of detection strategies and classification methods try to identify and categorize each ailment that affects the plant rather than focusing on a single disease among several illnesses and symptoms. This article describes a new support vector machine and image processing-enabled approach for detecting and classifying grape leaf disease. The given architecture includes steps for image capture, denoising, enhancement, segmentation, feature extraction, classification, and detection. Image denoising is conducted using the mean function, image enhancement is performed using the CLAHE method, pictures are segmented using the fuzzy C Means algorithm, features are retrieved using PCA, and images are eventually classed using the PSO SVM, BPNN, and random forest algorithms. The accuracy of PSO SVM is higher in performing classification and detection of grape leaf diseases.

Food needs are rising as the world's population grows. Similarly, the food processing industry is growing at an accelerated pace. Reduced food waste, better supply chain management, and increased delivery and storage of food are urgently required. These goals can be achieved to a large part with the use of artificial intelligence and machine learning. When a computer software can learn to anticipate future events without being programmed, it is known as machine learning. Supervised learning and unsupervised learning are two of the most common subcategories of machine learning. In this post, we'll look at how AI and machine learning are being used in the food and agricultural sector. Supply chain optimization, crop selection, logistics, food delivery, maintenance prediction in food processing machinery, detection of crop disease, smart irrigation, crop yield prediction, tracking of perishable food, soil data analysis, and weather data analysis are all major applications.