BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are suppl...
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BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities.
MATERIALS AND METHODS:In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance.
RESULT:The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies.
CONCLUSION:This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.
ABSTRACTOpen Learner Model (OLM) visualisations capture and display a learner’s learning state such as knowledge levels, learning progress, and misconceptions. Exposing learners to their own models offers learners a ...
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ABSTRACTOpen Learner Model (OLM) visualisations capture and display a learner’s learning state such as knowledge levels, learning progress, and misconceptions. Exposing learners to their own models offers learners a perspective about their learning state. Numerous benefits have been reported include providing more opportunities for learners to collaborate, enhancing learning outcomes, and prompting learners to be more self-regulated. We describe a new independent OLM tool, Doubtfire++ that we have designed to support Task-Oriented Portfolio teaching to raise student awareness of their progress and achievements, and to encourage self-regulated learning. The results of our evaluation show that Doubtfire++ helped the teaching staff in creating a supportive learning environment and students felt supported and rewarded for learning in a self-regulated manner. We have identified several OLM visualizations that were perceived to assist self-regulated learning. We also have gained insightful inputs and feedback that lead to expanding its application to a broader context.
WordPress is the world’s most popular content management system. In recent years, websites using WordPress have been a popular target for hackers due to the platform’s popularity. To solve these problems, users turn...
WordPress is the world’s most popular content management system. In recent years, websites using WordPress have been a popular target for hackers due to the platform’s popularity. To solve these problems, users turn to security plugins to help them deal with security threats. However, there is no in-depth security analysis of the WordPress plugin system. In this regard, the paper aims to analyze the work of the main security plugins, including code analysis, testing with different types of threats, checking the data that they collect, and evaluating their strengths and weaknesses. The working principles and implementations of modern security plugins have been described and the criteria for selecting the studied plugins have been defined. The results have been summarized and the approaches used by the different plugins have been compared.
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mits...
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni
Mobile cloud computing (MCC) refers to an infrastructure in which data processing and storage can take place far from mobile devices. The convergence of compact registration and network distributed computing has creat...
Mobile cloud computing (MCC) refers to an infrastructure in which data processing and storage can take place far from mobile devices. The convergence of compact registration and network distributed computing has created scalable distributed computing. This technology provides consumers a number of points of interest, similar to storage limits, reliability, scalability and access to real-time information. As a result, it is expanding steadily and is undoubtedly organised into a daily day-to-day life. The Cloud servers can be used for the preparation and storage of ***, in the current conditions, the secrecy of photos and information is generally important. In this paper, we concentrate mainly on the safe re-appropriation of photographs.
One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging...
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One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today’s organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.
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