Implementation of explainable artificial intelligence in commercial communication systems using micro systems.

The developments in the field of artificial intelligence (AI) and decision making systems are identified as virtuous models for banking and finance sector (BFS) applications. Even though AI provides great advantage in application changes it is essential to remodel the system using explainable artificial intelligence (XAI) design system. Also the standard sensing models provides appropriate monitoring values but huge size of sensors is considered as a major drawback.

Cybercrime: Identification and Prediction Using Machine Learning Techniques.

In the world of cyber age, cybercrime is spreading its root extensively. Supervised classification methods such as the support vector machine (SVM) and K-nearest neighbor (KNN) models are employed for the classification of cybercrime data. Likewise, the unsupervised mode of classification involves the techniques of K-means clustering, Gaussian mixture model, and cluster quasi-random via fuzzy C-means clustering and fuzzy clustering.

Implementation of a Heart Disease Risk Prediction Model Using Machine Learning.

Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease.

Sentiment Analysis on COVID-19 Twitter Data Streams Using Deep Belief Neural Networks.

Social media is Internet-based by design, allowing people to share content quickly via electronic means. People can openly express their thoughts on social media sites such as Twitter, which can then be shared with other people. During the recent COVID-19 outbreak, public opinion analytics provided useful information for determining the best public health response.

Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model.

Approximate computing is an upsurging technique to accelerate the process through less computational effort while keeping admissible accuracy of error-tolerant applications such as multimedia and deep learning. Inheritance properties of the deep learning process aid the designer to abridge the circuitry and also to increase the computation speed at the cost of the accuracy of results. High computational complexity and low-power requirement of portable devices in the dark silicon era sought suitable alternate for Complementary Metal Oxide Semiconductor (CMOS) technology.

A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts.

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem.

Automated Cardioailment Identification and Prevention by Hybrid Machine Learning Models.

Accurate prediction of cardiovascular disease is necessary and considered to be a difficult attempt to treat a patient effectively before a heart attack occurs. According to recent studies, heart disease is said to be one of the leading origins of death worldwide. Early identification of CHD can assist to reduce death rates.

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection.

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization.