Abstract This paper investigates whether Artificial Intelligence (AI) can predict whether a sequence of swaps applied to a finite set produces a complete cycle, defined as a permutation whose disjoint cycle decomposition consists of a single $n$-cycle. The task is useful for testing whether neural networks can learn combinatorial structure from permutation-based sequence data. The […]

Abstract Researchers commonly investigate how data properties influence the internal organization of convolutional neural networks, yet these effects are often inferred indirectly rather than examined through direct analysis of learned feature representations. Although data augmentation is widely used to increase dataset diversity, its influence on internal feature representations remains less clearly characterized. Specifically, how augmentation […]

Abstract Chemical leaks and contamination of the atmosphere are serious hazards in enclosed industrial areas if not detected. In the world, the failure to identify toxic gases and local hot spots directly leads to avoidable deaths in the workplace and to widespread damage of infrastructure. A multi-modal hazard detection system was designed to be semi-autonomous […]

Abstract This review explores the role of TP53 gene mutations in disrupting the cell cycle and their implications for cancer development. The primary objective was to investigate how alterations in the p53 tumor suppressor protein impact cellular regulatory mechanisms, ultimately leading to uncontrolled cell growth and tumorigenesis. To this end, a qualitative approach was adopted […]

Abstract Schizophrenia is a severe, chronic mental illness that affects about 1% of people globally. Due to schizophrenia’s psychotic symptoms, patients have a difficult time adhering to medications such as antipsychotics, which can negatively impact both their mental and physical health due to side effects and disorganized thoughts that arise after medication non-adherence. Furthermore, antipsychotics […]