A STUDY ON THE COMPLETENESS OF PATIENT MEDICAL DOCUMENTATION BY PHYSICIANS IN MULTISPECIALTY HOSPITALS
In multi-specialty hospitals, maintaining comprehensive and accurate medical records is a mechanical necessity for high-quality care, regulatory compliance, and medicolegal protection. While physicians are the primary authors of these clinical records, a focus on care execution over documentation often leads to critical gaps in recording patient pr...
By A. Jasmin, K. Ravichandran, K. Anandhi
METAHEURISTIC-BASED OPTIMIZATION OF COMPOSER DEEP LEARNING MODELS FOR SEPSIS PREDICTION AND CLASSIFICATION
Heterogeneous, high-dimensional, and complex clinical data have remained a key challenge for early sepsis detection in intensive care units. This paper introduces a Hybrid GAP-HMSOA-GNN-COMPOSER architecture that incorporates a graph-based relational model and metaheuristic optimization to learn and generate accurate, reliable sepsis predictions. T...
By K. Sameera, P. Amudhavalli
INFLUENCING FACTORS ON MIGRATION: EVIDENCE AND POLICY IMPLICATIONS FOR AN EMERGING COUNTRY
International migration from developing economies is increasingly driven by the interplay between economic incentives, social networks, and the institutional environment. The understanding of these drivers is central to policy design aimed at maximizing the benefits of labor mobility, minimizing human capital loss, and capturing positive impacts of...
By Dao Tuan Minh
ARTIFICIAL INTELLIGENCE IN AUDITING: EVALUATING THE IMPACT OF GENERATIVE AND PREDICTIVE MODELS ON AUDIT QUALITY AND EFFICIENCY
Artificial intelligence (AI) is significantly contributing to beneficial transformations in auditing by enhancing analytical capabilities, automating monotonous tasks, and refining decision-making processes. This piece of research looks into the effects of predictive and generative AI on the quality of auditing as well as its efficiency. Moreover, ...
By S. Edmund Christopher, Jaishu Antony, Zaheda Daruwala, Mahmoud Abouagwa
EFFICIENTNET-B0-BASED FRAMEWORK FOR AUTOMATED MULBERRY ROOT KNOT DISEASE CLASSIFICATION Based ON LEAF SYMPTOMS USING GRAD-CAM VISUALIZATION
The study is based on the idea of an innovative method of automated classification of mulberry root Knot diseases on the basis of the leaf symptoms through the EfficientMulberryNet-B0-GC model, which is a combination of EfficientNet-B0 and Grad-Cam visualization. The model aims to complement the lack of efficient and transparent disease detection i...
By N. Priyanka, K.G. Mohan
INTELLIGENT FINANCIAL DOCUMENT DIGITISATION AND CLASSIFICATION VIA A HYBRID ATTENTION-GUIDED DUAL PATH CNN WITH ATOMIC ORBITAL SEARCH OPTIMISATION
The financial institution uses different types of documents, including bank statements, cheques, income tax forms, salary slips, and electricity bills. Current approaches deal with either the preprocessing stage, text and feature extraction, or even classification separately and fail to perform efficiently on low-quality, handwritten, and visually ...
By Archana Lopes, Kolla Bhanu Prakash, Yu-Chen Hu
ASSESSING IFRS 15 IMPLEMENTATION EFFECTS ON REVENUE PRACTICES AND ACCOUNTING INFORMATION SYSTEMS IN UAE TELECOM COMPANIES E& AND DU
By comparing e& with du, the study examines how introducing the International Accounting Standard 15 (IFRS 15) affected revenue recognition, revenue measurement, and information systems across UAE telecommunications businesses. The comparison provides evidence from both companies. Because data lacked a normal distribution, the analysis employed...
By Ranya Riyadh Radhi, Hussein Talib Alhamada, Majid Ahmed AL Anssari
MEDIAN ATTRIBUTE HYBRID CLUSTERED MODEL USING PARTICLE SWARM OPTIMIZATION FOR NETWORK INTRUSION DETECTION
The rapid growth of cloud-based and large-scale network infrastructures has increased the complexity and frequency of cyber-attacks, demanding efficient and scalable intrusion detection systems (IDS). This paper will also attempt to create a better Network Intrusion Detection System (NIDS) by incorporating a Hybrid Median Attribute Clustering model...
By Rajasekhar Kaseebhotla, Raghava Rao, Mallikarjuna Rao
DEEP REINFORCEMENT LEARNING CONTROL OF SWARM UAVS FOR POST DISASTER CIVIL INFRASTRUCTURE ASSESSMENT
Rapid and accurate assessment of civic infrastructure following a natural or artificial disaster is essential to planning emergency response and recovery. This paper introduces a control system based on deep reinforcement learning (DRL) to coordinate unmanned aerial vehicle (UAV) swarms and methodically approach the post-disaster infrastructure ins...
By Moti Ranjan Tandi, Archana Mishra
PERFORMANCE ANALYSIS OF DOWN SYNDROME DETECTION SYSTEM USING ULTRA SOUND FETUS IMAGES AND DEEP LEARNING CLASSIFICATION METHODS
Down Syndrome (DS) is a genetic disorder due to a partial copy of the 21 st chromosome in the fetus, usually detected by invasive techniques like Amniocentesis and Chorionic Villus Sampling (CVS) which have chances of miscarriage. This paper suggests a completely automated and computer-aided method of non-invasive detection of DS by ultrasound fetu...
By V. Gokulakrishnan, S. Selvakumar