In recent years, breast cancer detection is an important area of concentration towards curative image dispensation and exploration. Detection of a disease at an early stage is an important factor in taking it to the next level of treatment. Accuracy plays an important role in the detection of disease. COA-T2FCM (Chimp Optimization Algorithm Based Type-2 Intuitionistic Fuzzy C-Means Clustering) is constructed for detection of such malignancy with the highest accuracy in this paper. The proposed detection process is designed with the combination of type-2 intuitionistic fuzzy c-means clustering in addition to oppositional function. In the type-2 intuitionistic fuzzy c-means clustering, the efficient cluster center can be preferred using the chimp optimization algorithm. Initially, the objective function of the type-2 intuitionistic fuzzy c-means clustering is considered. The chimp optimization algorithm is utilized to optimize the cluster center and fuzzifier in the clustering method. The projected technique is implemented, and in addition, performance metrics such as specificity, sensitivity, accuracy, Jaccard Similarity Index (JSI), and Dice Similarity Coefficient (DSC) are assessed. The projected technique is compared with the conventional technique such as fuzzy c means clustering and k mean clustering methods. The resulting method was also compared with existing methods to ensure the accuracy in the proposed method. The proposed algorithm is tested for its effectiveness on the mammogram images of the three different datasets collected from the Mini–Mammographic Image Analysis Society (Mini–MIAS), the Digital Database for Screening Mammography (DDSM), and Inbreast. The accuracy and Jaccard index score are generally used to measure the similarity between the proposed output and the actual cancer affected regions from the image considered. On an average the proposed method achieved an accuracy of 97.29% and JSI of 95%

Practical Learning is a great way of learning as it aids to increase a learner’s interest in a certain subject. It entails the use of visual aids to convey ideas even more effectively. Students can more easily understand and adapt the information from the subject, when they can visually see how it really works. Rather than just explaining the theory concepts, using animating slides, simulation software that explain the theory concepts easily can be used by the instructor to develop the stu-dent’s skill in the particular subject. Simulation tools provide significant cost savings in Education. This is because a topic can be taught effectively to large numbers of people without the need of buying expensive equipment. Like wireless networking labs, if we want to construct the lab with the equipment like wireless router, access points, different cabling it is more cost effective. Now in net, we can find out more simulation software for networking. One of the best simulation tools for Networking is Packet Tracer which can be freely downloadable from Cisco Networking Academy Students. It includes assessment task with automatic scoring and reporting. This paper presents the features of the simulation tool and analyzes the student’s performance over this simulation soft-ware with the given questionnaires. This study provides evidence in support of the instructional effectiveness of the use of Simulation software tool for the teachers and the learners who are interested in networking concepts.

Histopathology image classification plays a critical role in computer-aided diagnosis by supporting pathologists in disease detection and grading. With the rapid advancement of artificial intelligence, a wide range of machine learning, deep learning, and transformer-based models have been applied to histopathological image analysis. However, a systematic and fair comparison of these approaches under a unified experimental setting remains limited. In this study, we present a comprehensive performance and generalization analysis of classical machine learning classifiers, convolutional neural network (CNN) models, and vision transformer-based architectures for histopathology image classification. Publicly available benchmark datasets were used to evaluate the models using standardized preprocessing, training protocols, and evaluation metrics. The comparative analysis highlights the strengths and limitations of each category of methods in terms of classification accuracy, robustness, and computational complexity. Experimental results demonstrate that deep learning and transformer-based models consistently outperform traditional machine learning approaches, while transformer models show improved generalization capability on complex tissue patterns. The findings of this study provide practical insights for selecting suitable classification models in histopathology-based diagnostic applications and contribute to the development of reliable medical imaging decision-support systems.