A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage. The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people. This article presents detection of pancreatic cancer in CT scan images using machine PSO SVM and image processing. The Gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images. The K means algorithm uses a partitioning technique to separate the image into its component parts. The process of identifying objects in an image and determining the regions of interest is aided by image segmentation. The PCA method is used to extract important information from digital photographs. PSO SVM, naive Bayes, and AdaBoost are the algorithms that are used to perform the classification. Accuracy, sensitivity, and specificity of the PSO SVM algorithm are better.

The primary contributor to lung cancer is an abnormal proliferation of lung cells. Tobacco usage and smoking cigarettes are the primary contributors to the development of lung cancer. The most common forms of lung cancer fall into two distinct types. Non-small-cell lung cancers and small-cell lung cancers are the two primary subtypes of lung cancer. A computed tomography, or CT, scan is an essential diagnostic technique that may determine the kind of cancer a patient has, its stage, the location of any metastases, and the degree to which it has spread to other organs. Other diagnostic tools include biopsies and pathology tests. The creation of algorithms that allow computers to gain information and abilities by seeing and interacting with the world around them is the core emphasis of the field of machine learning. This article demonstrates how to detect lung cancer via the use of machine learning by using improved feature selection and image processing. Image quality may be improved with the help of the CLAHE algorithm. The K Means technique is used in order to segment a picture into its component components. In order to determine which traits are beneficial, the PSO algorithm is utilised. The photos are then categorised using the SVM, ANN, and KNN algorithms respectively. It uses images obtained from a CT scan. When it comes to detecting lung cancer, PSO SVM provides more accurate results.

The process of teaching and learning is the most powerful tool that teachers and professors have, because it is the main way that students change in the ways that teachers and professors want them to. TQM is recommended as a way to control, monitor, and improve the quality of teaching and learning strategies in the classroom. In line with the principles of TQM, evaluations of things like results and feedback are used to improve teaching and learning. The level of academic success that a program's students have may say a lot about how good that programme is. You can predict how well a student will do in school in the future by looking at how well they did in the past. After more research, it might be possible to find a link between the students' grades and their skills and interests. When teachers have this kind of information, they are better able to focus on the students who are having the most trouble. This article shows how to use feature selection and machine learning to improve the quality of teaching and learning in higher education by predicting how well a student will do in school. The performance of university students is used as an input to a classification model. First, ant colony optimization is used to choose the most important features. Then, KNN, Naive Bayes, and decision tree algorithms are used to classify the chosen data. Based on accuracy, recall, and F1 score, KNN performs better.

A tumour, as the name implies, is a tumorous growth of tissue anywhere in the body. There are various types of tumours, each with its own set of characteristics and treatment plan. The goal of this study is to create a reliable algorithm for detecting tumours in brain MRI images. Image segmentation is critical for detecting brain tumours. One of the most difficult, but crucial, processes is detecting a brain tumour. As a result, accurate segmentation of Magnetic Resonance Imaging (MRI) images is critical for subsequent diagnosis. The ongoing research into automatic detection of brain structures is motivated by a desire to learn more about the connections between the anatomy of brain tissues and various mental and physical disorders in humans. These days, medical professionals are particularly interested in computer-aided technologies that can identify and characterise certain organs or medical characteristics. Using image processing and machine learning, this study proposes a strategy for the early and accurate detection of brain tumours. The SVM, ANN, and ID3 algorithms are all utilised in some capacity within the context of this framework's procedures for extracting features and segmenting images. Metrics such as accuracy, specificity, and sensitivity are utilised in the evaluation process so that we can determine how well an algorithm performs.

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