Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.

Recent development in the field of human–computer interaction has led renewed interest in dynamic hand gesture segmentation based on gesture recognition system. Despite its long clinical success, dynamic hand gesture segmentation using webcam vision becomes technically challenging and suffers the problem of non-accurate and poor hand gesture segmentation where the hand region is not integral due to complicated environment, partial occlusion and light effects. Therefore, for segmenting complete hand gesture region and improving the segmentation accuracy, this study proposes a combination of four modified visual features segmentation procedures, which are skin, motion, skin moving as well as contour features and fast marching method. Quantitative measurement was performed for evaluating hand gesture segmentation algorithm. Besides, qualitative measurement was done to conduct a comparison based on segmentation accuracy with previous studies. Consequently, the experiment results showed a great enhancement in hand area segmentation with a high accuracy rate of 98%.

COVID-19 was first reported in China Wuhan and rapidly grown up to more than 58 countries based on the World Health Organization (WHO). Well ahead of any health emergency, the health care server has the ability to access these data via authorization and then s/he performs necessary actions. In order to protect medical data from malicious activities, authentication is the starting point for this. Authentication systems represent a network support factor to reduce ineffective users and radically eliminate phishing because authentication determines the identity of the real user. Many schemes and technologies have been suggested for authentication in wireless body area networks (WBANs). In this paper, we suggest a strong dynamic password authentication system for WBANs. We adopt a (different/new) way to calculate a password and make it coherent and dynamic for each login session. Our work also provides additional security properties to get rid of hub node impersonation attacks and resolve key escrow issues. Our scheme resist fishing attach which keep patient from any illegal change of drugs. By comparison, the proposed scheme is considered active and has strong security based on formal security analysis tools such as AVISPA.

COVID-19 vaccination topic has been a hot topic of discussions on social media platforms wondering its effectiveness against the SARS-COV-2 virus. Twitter is one of the social media platforms that people widely lunched to express and share their thoughts about different issues touching their daily life. Though many studies have been undertaken for COVID-19 vaccine sentiment analysis, they are still limited and need to be updated constantly. This paper conducts a system for COVID-19 vaccine sentiment analysis based on data extracted from Twitter platform for the time interval from 1st of January till the 3rd of Sep. 2021, and by using deep learning techniques. The introduced system proposes to develop a model architecture based on a deep bidirectional long short-term memory (LSTM) neural network, to analyze tweets data in the form of positive, neutral, and negative. As a result, the overall accuracy of the developed model based on validation data is 74.92%. The obtained outcomes from the sentiment analysis system on collected tweets-data of COVID-19 vaccine revealed that neutral is the prominent sentiment with a rate of 69.5%, and negative sentiment has less rate of tweets reached 20.75% while the positive sentiment has a lesser rate of tweets reached of 9.67%.