Machine learning (ML) can enhance a dermatologist’s work, from diagnosis to customized care. The development of ML algorithms in dermatology has been supported lately regarding links to digital data processing (e.g., electronic medical records, Image Archives, omics), quicker computing and cheaper data storage. This article describes the fundamentals of ML-based implementations, as well as future limits and concerns for the production of skin cancer detection and classification systems. We also explored five fields of dermatology using deep learning applications: (1) the classification of diseases by clinical photos, (2) der moto pathology visual classification of cancer, and (3) the measurement of skin diseases by smartphone applications and personal tracking systems. This analysis aims to provide dermatologists with a guide that helps demystify the basics of ML and its different applications to identify their possible challenges correctly. This paper surveyed studies on skin cancer detection using deep learning to assess the features and advantages of other techniques. Moreover, this paper also defined the basic requirements for creating a skin cancer detection application, which revolves around two main issues: the full segmentation image and the tracking of the lesion on the skin using deep learning. Most of the techniques found in this survey address these two problems. Some of the methods also categorize the type of cancer too.

Many people suffer from Parkinson’s disease globally, a complicated neurological condition caused by the deficiency of dopamine, an organic chemical responsible for regulating movement in individuals. Patients with Parkinson face muscle stiffness or rigidity, tremors, vocal impairment, slow movement, loss of facial expressions, and problems with balance and coordination. As there is no cure for Parkinson, early diagnosis can help prevent the progression of this disease. The study explores the potential of vocal measures as significant indicators for early prediction of Parkinson. Different machine learning models such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT) are used to detect Parkinson using voice measures and differentiate between the healthy and Parkinson patients. The dataset contains 195 vocal recordings from 31 patients. The Synthetic Minority Over-Sampling Technique (SMOTE) is used for handling class imbalance to improve the performance of the models. The Principal Component Analysis (PCA) method was used for feature selection. The study uses different parameters to evaluate the model’s classification results. The results highlight RF as the most effective model with an accuracy of 94% and a precision of 94%. In addition, SVM achieves an accuracy score of 92%, and precision of 91%. However, with the PCA method, SVM achieves an accuracy of 89%, 92%, and 87% for RF and DT respectively. This study highlights the significance of using vocal features along with advanced machine learning methods to reliably diagnose Parkinson’s disease, considering the challenges associated with early detection.

In this study, a weighted federated learning approach is proposed for electrocardiogram (ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data distribution among multiple clients in federated learning settings. The weight of each client is dynamically adjusted according to its contribution to the global model improvement. Experiments on public ECG datasets show that the proposed approach outperforms traditional federated learning and centralized learning methods in terms of accuracy and robustness. On the client side, the suggested federated learning (FL) approach had an accuracy of 0.93, sensitivity of 0.98, specificity of 0.82, miss classification rate of 0.07, precision of 0.06, FPR of 0.01, and FNR of 0.01. FL has 0.98 accuracy, 0.99 sensitivity, 0.91 specificity, 0.02 miss classification rate, 0.10 precision, 0.01, FPR, and 0.01 FNR on the server. The server-side federated learning approach outperforms the client-side in accuracy, sensitivity, specificity, miss classification rates, and precision. The results indicate that the proposed weighted federated learning approach is a promising solution for ECG arrhythmia classification in a distributed environment. In short, the proposed federated learning approach applied to ECG arrhythmia detection aims to address privacy concerns and improve accuracy, while still maintaining the centralized framework and advanced algorithmic approach.

The demand for recommender systems in E-commerce industry has increased tremendously. Efficient recommender systems are being proposed by different E-business companies with the intention to give users accurate and most relevant recommendation of products from huge amount of information. To improve the performance of recommender systems, various stochastic variants of gradient descent based algorithms have been reported. The scalability requirement of recommender systems needs algorithms with fast convergence to generate recommendations of specific items. Using the concepts of fractional calculus, an efficient variant of the stochastic gradient descent (SGD) was developed for fast convergence. Such fractional SGD (F-SGD) is further accelerated by adding a momentum term, thus termed as momentum fractional stochastic gradient descent (mF-SGD). The proposed mF-SGD method is shown to offer improved estimation accuracy and convergence rate, as compared to F-SGD and standard momentum SGD for different proportions of previous gradients, fractional orders, learning rates and number of features.

Through the expeditious expansion of the wireless network, the unlicensed bandwidth-based devices are growing substantially as compared to the present vacant bandwidth. Cognitive radio networks present a proficient solution to the spectrum shortage diminution hitch by allowing the usage of the vacant part of the spectrum that is not currently in use of the Primary User licensed bandwidth to the secondary user or cognitive radio user. Spectrum management procedure in cognitive radio network comprises of spectrum sharing, sensing and handoff. Spectrum handoff plays a vital role in spectrum management and primarily focuses on single handoff strategies. This paper presents a primary user traffic pattern-based opportunistic spectrum handoff (PUTPOSH) approach to use in the cognitive radio networks. PUTPOSH permits a secondary user to sense the arrival of a primary user and use an opportunistic handoff scheme. The opportunistic handoff scheme firstly detects the arrival of the primary users by energy detection sensing and secondly, it allows a cognitive radio user to decide whether to do handoff or not contingent upon the overall service time to reduce the unused handoffs. The handoffs can either be reactive or proactive based on the arrival rate of the primary user. The simulation results show that the presented PUTPOSH approach (a) minimizes the number of handoffs and the overall service time, and (b) maintains the channel utilization and throughput of the system at a maximal point.