MELANOMA DETECTION BASED ON DEEP LEARNING NETWORKS

Our main objective is to develop a method for identifying melanoma enabling accurate assessments of patient’s health. Skin cancer, such as melanoma can be extremely dangerous if not detected and treated early. Detecting skin cancer accurately and promptly can greatly increase the chances of survival. To achieve this, it is important to develop a computer-aided diagnostic support system. In this study a research team introduces a sophisticated transfer learning model that utilizes Resnet50 to classify melanoma. Transfer learning is a machine learning technique that takes advantage of trained models, for similar tasks resulting in time saving and enhanced accuracy by avoiding the need to train from scratch. The Resnet50 is a type of network that can distinguish between cancerous skin lesions in each sample. To evaluate its performance, we used data from the melanoma cancer dataset. However, the dataset has a percentage of samples which creates an imbalance between the classes. We addressed this issue by making the dataset more diverse through data augmentation techniques. In our project we implemented the Resnet50 pretrained model with learning rates and weight decay. This model consists of 50 layers organized into blocks that include batch normalization and skip connections (known as connections). We adjusted the depth of the model to improve its accuracy. Our experimental results demonstrate that our proposed deep learning technique performs better in terms of accuracy compared to state of the art algorithms in this field. iii The model achieves an accuracy of 91.70%, with a learning rate of 0.0001 and a model depth of 34. By tuning hyperparameters using RESNET 50 we can further enhance the accuracy of our trained models

Estimating Air Pollution Levels Using Machine Learning

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air quality estimation techniques, which need several instruments and setup and have large financial expenditures for equipment acquisition and maintenance

Stitching proteins into membranes, not sew simple

Most integral membrane proteins located within the endomembrane system of eukaryotic cells are first assembled co-translationally into the endoplasmic reticulum (ER) before being sorted and trafficked to other organelles. The assembly of membrane proteins is mediated by the ER translocon, which allows passage of lumenal domains through and lateral integration of transmembrane (TM) domains into the ER membrane. It may be convenient to imagine multi-TM domain containing membrane proteins being assembled by inserting their first TM domain in the correct orientation, with subsequent TM domains inserting with alternating orientations. However a simple threading model of assembly, with sequential insertion of one TM domain into the membrane after another, does not universally stand up to scrutiny. In this article we review some of the literature illustrating the complexities of membrane protein assembly. We also present our own thoughts on aspects that we feel are poorly understood. In short we hope to convince the readers that threading of membrane proteins into membranes is 'not sew simple' and a topic that requires further investigation

Transmembrane segments of nascent polytopic membrane proteins control cytosol/ER targeting during membrane integration

Vastly different folded transmembrane segments of nascent multispanning membrane proteins each induce structural changes in the ribosome tunnel and translocon that target the loops of the growing polypeptide alternately into the cytosol or ER lumen

Polytopic membrane protein folding at L17 in the ribosome tunnel initiates cyclical changes at the translocon

Interaction between L17 in the ribosome tunnel and folded nascent chain transmembrane segments during multi-spanning membrane protein synthesis triggers structural rearrangements in the ribosome that cause switching between cytosolic and ER lumenal targeting of the growing polypeptide