BRIDGING MIND AND EMOTION: ENHANCING STUDENT LEARNING THROUGH COGNITIVE AND EMOTIONAL INTELLIGENCE

This study examines the interrelated nature of cognitive and emotional intelligence and their combined influence on student learning outcomes. Cognitive intelligence plays a crucial role in developing critical thinking and problem-solving abilities, whereas emotional intelligence supports essential life skills such as self-awareness, empathy, and effective interpersonal communication. Adopting a mixed-methods approach, the research utilized surveys…

DEEP LEARNING APPROACH FOR CROSS-CROP PLANT DISEASE DETECTION

One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions. These diseases can decimate crops, disrupt food supply chains, and escalate the risk of food shortages, underscoring the urgency of implementing robust strategies to safeguard the world’s food sources. Deep learning methods have…

HARNESSING XGBOOST TO UNCOVER THE IMPACT OF EMOTIONAL INTELLIGENCE ON ACADEMIC RESULTS

Emotional intelligence (EI) has been increasingly recognized as a critical factor influencing academic success among college students. This study investigates the impact of EI components—self-awareness, self-regulation, motivation, empathy, and social skills—on academic achievements using the XGBoost algorithm, a powerful machine learning technique known for its high predictive accuracy. The primary objective of this work is…

ASSESSING DYNAMIC DQN MODELS FOR EFFICIENT CLASSIFICATION ON SKIN DISEASE DATA

The field of dermatology has increasingly embraced artificial intelligence (AI) and machine learning techniques to improve the accuracy and efficiency of diagnosing and classifying skin diseases. Among these techniques, deep learning models, particularly convolutional neural networks (CNNs), have demonstrated remarkable performance in medical image analysis. However, CNNs typically require large volumes of labeled data to…

ENHANCED MULTI-CLASS SKIN CANCER CLASSIFICATION THROUGH SCD-NET ON DERMOSCOPY DATA

steadily increasing in recent years. Early and accurate diagnosis is crucial for effective treatment and improved patient outcomes. However, communication barriers between patients and dermatologists often result in a knowledge gap regarding the symptoms and stages of various skin conditions. To address this issue, this study introduces a novel deep learning–based skin disease classification model…

OPTIMIZING SMART AGRICULTURE WITH CUTTING-EDGE DEEP LEARNING ARCHITECTURES

Agriculture remains a cornerstone of the Indian economy, contributing substantially to national growth and food security. Enhancing agricultural productivity demands improvements in both the quality and quantity of crop yield while minimizing operational costs. One of the major challenges faced by farmers is the prevalence of weeds and pests, which adversely affect crop health and…

A DETAILED REVIEW OF STATE-OF-THE-ART CLUSTERING PROTOCOLS FOR WIRELESS SENSOR NETWORKS IN IOT

The rapid expansion of Internet-connected devices now includes not only smartphones but also diverse systems capable of data collection and operation in outdoor environments. Within this context, Wireless Sensor Networks (WSNs) play a pivotal role in the Internet of Things (IoT) ecosystem, offering intelligence, adaptability, and reliability. WSNs are typically composed of numerous sensors with…