Extreme caution should be exercised while dealing with any form of skin cancer, but especially malignant melanoma. It’s also on the rise, especially among whites who spend a lot of time outdoors in the sun. Because early diagnosis of melanoma may be beneficial and curative, it is crucial that it be detected at an early stage to improve survival rates. Accurate automatic skin lesion segmentation is in high demand due to the rapid proliferation of skin cancer. While deep learning models like CNN were widely utilized to enable proper segmentation, current encoder-decoder designs based on compactly connected networks (DenseNet) and residual networks (ResNet) were applied for skin lesion tasks. Complex parameter settings, a lack of multi-scale data, and an absence of appropriate information in pre-trained features all have an effect on the performance of skin lesion segmentation. This research proposes a system for segmenting skin lesions utilizing the UNet and Residual UNET (ResUNet) architectures to solve these issues. CNNs, which consist of encoders and decoders, form the basis of these designs. The architecture employs UNet and ResUNet to guarantee high-quality lesion segmentation at all times. The proposed models are evaluated on ISIC2018 and HAM10000 lesion pictures. Accuracy, dice coefficient, Jaccard index, sensitivity, and specificity are used to assess the performance of the models. The models’ efficacy is evaluated in light of state-of-the-art approaches.

Across the globe, deep learning methodologies are rapidly transforming the medical field. Among the rapidly expanding domains, medical image categorization stands out as a crucial area for developing effective intelligent systems. Consequently, our research employs various CNN variations to establish a robust and reliable model for medical image categorization. Within this study, we utilize two distinct medical imaging datasets: one comprising malaria cell images and the other featuring chest X-ray images for pneumonia diagnosis. To enhance the model's accuracy, we fine-tune its performance by adjusting parameters such as layer count and activation functions. This approach empowers researchers to identify optimal CNN parameters for image classification and observe how model behavior evolves with changing image types. The presented models undergo validation using precision metrics, including F -score, specificity, and accuracy. Notably, in the malaria and pneumonia datasets, our model achieves accuracy rates of 96 % and 95%, respectively.

This work proposes a fault detection and classification method using spectral entropy and IDCNN. The proposed method uses one end current signals for analyzing the fault and no fault scenarios. The current signals are recorded with 1kHz sampling frequency and processed using spectral entropy. The input current features are then given to 1D-CNN network for fault detection and classification. The proposed method has been validated varying fault and no fault situations. The proposed method has specificity and sensitivity of 100 % .

Prognosis of illnesses is a difficult problem these days throughout the globe. Elder people of twenty years and over are taken into consideration to be laid low with this sickness now a days. For example, human beings having HbA1c level more than 6.5% are diagnosed as infected with diabetic diseases. This paper uses IoT to evaluate threat factors which have been similar to heart diseases which are not treated properly. Diagnosis, prevention of heart disease may be done by use of machine learning (ML). There has been an extensive disconnect among Machine Learning architects, health care researchers, patients and physicians in their technology. This paper intends to perform an in-intensity evaluation on Machine Learning to make us of new advance technologies. Latest advances within the development of IoT implanted devices and other medicine delivery gadgets, disease diagnostic methods and other medical research have considerably helped human beings diagnosed heart diseases. New soft computing models can be helpful for remedy of various heart diseases. The Food and Drug Administration (FDA) employs several particularly creative thoughts to get their capsules to the client. Artificial Neural Community offers a first-rate chance to deal with heart diseases with advance IoT and cloud applications.

Due to advancement in Science and Technologies there are enormous amount of data available on internet. A large amount of structured, semi-structured and unstructured data is being created at a very rapid speed every day from heterogeneous sources like reviews, ratings, feedbacks, shopping details, etc., it is termed as Big Data. This data generated from different users share many common patterns which can be filtered and analysed to give some recommendation regarding the product, goods or services in which a user is interested. Recommendation systems are the software tools used to give suggestions to users on the basis of their requirements. Many people are not so much aware of different profitable and economical alternatives before using their money for goods or services. They are not so intelligent that they can quickly compare and judge that which product or service is better. The presented paper proposed a recommended system for management and utilisation of three components of salary i.e. saving, investment and expenditure. Many savings and investment consulting systems are available but no system provides effective and efficient recommendation regarding management and beneficial utilisation of salary. The advantage of proposed recommended system is that it provides better suggestion to a person for saving, expenditure and investment of their salary which in turns maximises their wealth. Due to enormous amount of data involved, Apache Hadoop framework is used for distributed processing. Apache Mahout is used for analysing the data and implementation of the recommender system.