Detection and discrimination of cracked digitized paintings based on image processing methodology

With the passage of time, paintings can be damaged, and common deteriorations found in ancient paintings include cracking. Cracks can be caused by many factors, such as ageing, drying, and mechanical factors. The detection and restoration of crack formation on the earliest digitized paint surface concede great significance and safety for cultural heritages. particular, this paper is based on image processing methodology to detect and discriminate cracks. First, images are enhanced in the preprocessing stage for further processing. Then two proposed algorithms are employed to detect and separate medium and thin crack images. The experiments show better results compared with previous work. This study also confirms that the proposed image processing algorithms are an efficient and robust tool for the detection and discrimination of cracks

DeepCervixNet: an advanced deep learning approach for cervical cancer classification in Pap smear images

Cervical cancer is among the leading causes of female mortality, emphasizing the significance of early detection and treatment to prevent its spread. While Pap smear images are widely used for cervical cancer screening, the manual diagnostic method is timeconsuming and prone to error. The research article introduces DeepCervixNet, an innovative automated computerized approach designed for detecting cervical cancer in Pap smear images. In this study, we enhance ResNet101 and DenseNet169, state-of-the-art Convolutional Neural Network (CNN) architectures, by integrating the sequence and excitation (SE) blocks. Subsequently, Ensemble learning is employed to utilize the extracted features and classify the final output. The Harlev dataset was employed to test our model, with Gaussian smoothing and median filtering applied for image enhancement. This resulted in an overall improvement in the performance of the model. DeepCervixNet had an accuracy of 99.89% in cervical cells. The study’s findings validate our model’s robustness and efficacy, proving its superiority over a majority of current state-of-the-art models used to classify cervical cells, including standard ResNet and DenseNet architectures without SE block

Pervasive learning environment for educational makerspaces with emerging technologies and teaching and learning transformation

In 21st century the technological innovation has reshaped our educational system. Different educational methods and environments are used to facilitate students and teachers. Educational makerspaces are also one of them. This new trend has shifted educational system to student centered instead of teacher centered. Pervasive Learning (P-Learning) environment for educational makerspaces is one of the emerging environmentsfor teaching and learning due to price reduction in handheld devices’, facility for sharing the technological resources, support, and smartness of smartphone technology. Educational Makerspaces environment provide the facility to teachers and learners to create a virtual environment fully equipped with latest ICT tools to share ideas, to perform experiments and collaborate with each other whenever and wherever they are. Thus, P-learning for educational makerspaces can access and share the resources for teaching and learning material beyond the boundaries of the traditional classroom arrangement. This paper presents the idea of P-learning environment for educational makerspaces which is not limited to a single geographic location or mobile or location-based technologies; rather, it accesses, shares the resources, and facilitates teaching and learning from anywhere and at any time with any handheld device means 24*7*12. The purpose of this paper is to propose a fully equipped makerspace classroom so that the students from remote areas or who cannot afford laboratory expenses can access virtually labs, share their ideas, collaborate with each other, and perform experiment