Subset Of ML Research Articles

A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation.

Atrial fibrillation [AF] is the most common arrhythmia encountered in clinical practice and significantly increases the risk of stroke, peripheral embolism, and mortality. With the rapid advancement in artificial intelligence [AI] technologies, there is growing potential to enhance the tools used in AF detection and diagnosis. This scoping review aimed to synthesize the current knowledge on the application of AI, particularly machine learning [ML], in identifying and diagnosing AF in clinical settings. Following the PRISMA ScR guidelines, a comprehensive search was conducted using the MEDLINE, PubMed, SCOPUS, and EMBASE databases, targeting studies involving AI, cardiology, and diagnostic tools. Precisely 2635 articles were initially identified. After duplicate removal and detailed evaluation of titles, abstracts, and full texts, 30 studies were selected for review. Additional relevant studies were included to enrich the analysis. AI models, especially ML-based models, are increasingly used to optimize AF diagnosis. Deep learning, a subset of ML, has demonstrated superior performance by automatically extracting features from large datasets without manual intervention. Self-learning algorithms have been trained using diverse data, such as signals from 12-lead and single-lead electrocardiograms, and photoplethysmography, providing accurate AF detection across various modalities. AI-based models, particularly those utilizing deep learning, offer faster and more accurate diagnostic capabilities than traditional methods with equal or superior reliability. Ongoing research is further enhancing these algorithms using larger datasets to improve AF detection and management in clinical practice. These advancements hold promise for significantly improving the early diagnosis and treatment of AF.

Artificial Intelligence: The Future

Artificial intelligence is the intelligence of machines or software, as opposed to the intelligence of humans or animals. It is also the field of study in computer science that develops and studies intelligent machines. "AI" may also refer to the machines themselves. AI is not a new for the scientist, it was introduce in 1943 with artificial neurons model and get popular in 1950 due to “Turting test” the test was done to get answer that machine can think?, purposed by Alan Turing. Basically AI is categorized into three types, Artificial Narrow Intelligence, Artificial General Intelligence and Artificial Super Intelligence. Deep learning and machine learning is major subfield of Artificial intelligence. DL as a subset of ML, which is also another subset of AI. Therefore, AI is the all-encompassing concept that initially erupted. Application of AI fields are Healthcare, Business, Education, Agriculture, Finance, Law, Entertainment and media, Software coding and IT processes, Security, Manufacturing, Banking and Transportation. In reference to Job creation or distraction, Artificial Intelligence is not job killer but a job category killer”. In the latest report (May 2023) on The Future of Jobs, the World Economic Forum (WEF) predicts the creation of 69 million jobs by 2027 thanks to AI, but also the destruction of 89 million jobs. In context to intelligence level, Jan. 2022, Age of IQ level of AI was as 7 year Children and in Dec. 2022, Age of IQ level of AI was as 9 year Children. India is emerging market in global and it has around 12 % of work could be automated by AI. In India more than 2000 startup are related to AI and 90000 plus AI Professional work in India.The economic impact of AI, for select G20 countries and estimates AI to boost India’s annual growth rate by 1.3 percentage points by 2035. AI has potential to add 1 trillion to India’s economy in 2035. We are going to enter into new technological world, it’s may be our fortune or misfortune. Key Words: - Artificial Intelligence, Machine learning, Job, India

Optimizing ML with run-time code generation

We describe the design and implementation of a compiler that automatically translates ordinary programs written in a subset of ML into code that generates native code at run time. Run-time code generation can make use of values and invariants that cannot be exploited at compile time, yielding code that is often superior to statically optimal code. But the cost of optimizing and generating code at run time can be prohibitive. We demonstrate how compile-time specialization can reduce the cost of run-time code generation by an order of magnitude without greatly affecting code quality. Several benchmark programs are examined, which exhibit an average cost of only six cycles per instruction generated at run time.

Proofs-as-Programs as a Framework for the Design of an Analogy-Based ML Editor

Abstract. C Y NTHIA is a transformation-based editor for a functional subset of ML that lies somewhere between a structure editor and a framework for formal program development. Users construct programs from existing code by applying editing commands that make a semantic analysis of the program's behaviour, e.g., whether it is terminating. All analysis is done using the Oyster system, which is an implementation of proofs-as-programs. We concentrate on identifying analyses that can be done fully automatically (e.g., using a decision procedure) and hence can be hidden from the user. As a result, C Y NTHIA represents progress towards a goal of program editors that make an intelligent analysis of their code, but in a way that requires no extra input from the programmer.

A slicing-based approach for locating type errors

The effectiveness of a type-checking tool strongly depends on the accuracy of the positional information that is associated with type errors. We present an approach where the location associated with an error message e is defined as a slice P e of the program P being type-checked. We show that this approach yields highly accurate positional information: P e is a program that contains precisely those program constructs in P that caused error e . Semantically, we have the interesting property that type-checking P e is guaranteed to produce the same error e . Our approach is completely language-independent and has been implemented for a significant subset of Pascal. We also report on experiments with object-oriented type systems, and with a subset of ML.

Optimizing ML with run-time code generation

We describe the design and implementation of a compiler that automatically translates ordinary programs written in a subset of ML into code that generates native code at run time. Run-time code generation can make use of values and invariants that cannot be exploited at compile time, yielding code that is often superior to statically optimal code. But the cost of optimizing and generating code at run time can be prohibitive. We demonstrate how compile-time specialization can reduce the cost of run-time code generation by an order of magnitude without greatly affecting code quality. Several benchmark programs are examined, which exhibit an average cost of only six cycles per instruction generated at run time.