A customized convolutional neural network-based approach for weeds identification in cotton crops.

Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production.

Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.

Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields.

Status of Inappropriate Complementary Feeding and Its Associated Factors Among Infants of 9-23 Months.

Inappropriate complementary feeding during the first two years of life significantly impacts children's health, increasing risks of malnutrition and illness. : This study investigates factors influencing early feeding patterns among 600 mothers of children aged 9-23 months in selected hospitals in Punjab, Pakistan. Using a structured questionnaire, data were collected and analyzed, with associations measured by odds ratios (ORs) and 95% confidence intervals (CIs).

Ensemble genetic and CNN model-based image classification by enhancing hyperparameter tuning.

Model optimization is a problem of great concern and challenge for developing an image classification model. In image classification, selecting the appropriate hyperparameters can substantially boost the model's ability to learn intricate patterns and features from complex image data. Hyperparameter optimization helps to prevent overfitting by finding the right balance between complexity and generalization of a model.

Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.

Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing of mother and fetus. Monitoring fetal health through pregnancy is crucial for identifying and addressing potential risks and complications that may arise. Early detection of abnormalities and deviations in fetal health can facilitate timely interventions to mitigate risks and improve outcomes for the mother and fetus.

Novel transfer learning based bone fracture detection using radiographic images.

A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture can take anywhere from one month to one year, leading to significant economic and psychological challenges for patients.

FacialNet: facial emotion recognition for mental health analysis using UNet segmentation with transfer learning model.

Facial emotion recognition (FER) can serve as a valuable tool for assessing emotional states, which are often linked to mental health. However, mental health encompasses a broad range of factors that go beyond facial expressions. While FER provides insights into certain aspects of emotional well-being, it can be used in conjunction with other assessments to form a more comprehensive understanding of an individual's mental health.

Advancement in medical report generation: current practices, challenges, and future directions.

The correct analysis of medical images requires the medical knowledge and expertise of radiologists to understand, clarify, and explain complex patterns and diagnose diseases. After analyzing, radiologists write detailed and well-structured reports that contribute to the precise and timely diagnosis of patients. However, manually writing reports is often expensive and time-consuming, and it is difficult for radiologists to analyze medical images, particularly images with multiple views and perceptions.

Fairness-enhancing classification methods for non-binary sensitive features-.

Especially if artificial intelligence (AI)-supported decisions affect the society, the fairness of such AI-based methodologies constitutes an important area of research. In this contribution, we investigate the applications of AI to the socioeconomically relevant infrastructure of water distribution systems (WDSs). We propose an appropriate definition of protected groups in WDSs and generalized definitions of group fairness, applicable even to multiple non-binary sensitive features, that provably coincide with existing definitions for a single binary sensitive feature.

Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops.

Plant stress reduction research has advanced significantly with the use of Artificial Intelligence (AI) techniques, such as machine learning and deep learning. This is a significant step toward sustainable agriculture. Innovative insights into the physiological responses of plants mostly crops to drought stress have been revealed through the use of complex algorithms like gradient boosting, support vector machines (SVM), recurrent neural network (RNN), and long short-term memory (LSTM), combined with a thorough examination of the TYRKC and RBR-E3 domains in stress-associated signaling proteins across a range of crop species.

Atypical Motor-Predominant Presentation of Subacute Combined Degeneration of Spinal Cord Due to Vitamin B12 Deficiency: A Case Report of Stroke Mimicry in the Absence of Classic Symptoms.

Vitamin B12 deficiency is a prevalent condition that can lead to serious neurological disorders, including subacute combined degeneration (SCD) of the spinal cord, which can result in lasting damage if not promptly treated. This report discusses a unique case of a 53-year-old female patient who presented with a one-week history of gait instability and falls, ultimately diagnosed with SCD due to severe vitamin B12 deficiency. Notably, the patient exhibited an atypical presentation, lacking classic symptoms such as paraesthesia and hematologic abnormalities, which often accompany B12 deficiency.

Enhanced interpretable thyroid disease diagnosis by leveraging synthetic oversampling and machine learning models.

Thyroid illness encompasses a range of disorders affecting the thyroid gland, leading to either hyperthyroidism or hypothyroidism, which can significantly impact metabolism and overall health. Hypothyroidism can cause a slowdown in bodily processes, leading to symptoms such as fatigue, weight gain, depression, and cold sensitivity. Hyperthyroidism can lead to increased metabolism, causing symptoms like rapid weight loss, anxiety, irritability, and heart palpitations.

Virtual histopathology methods in medical imaging - a systematic review.

Virtual histopathology is an emerging technology in medical imaging that utilizes advanced computational methods to analyze tissue images for more precise disease diagnosis. Traditionally, histopathology relies on manual techniques and expertise, often resulting in time-consuming processes and variability in diagnoses. Virtual histopathology offers a more consistent, and automated approach, employing techniques like machine learning, deep learning, and image processing to simulate staining and enhance tissue analysis.

Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization.

With the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce.

StackIL10: A stacking ensemble model for the improved prediction of IL-10 inducing peptides.

Interleukin-10, a highly effective cytokine recognized for its anti-inflammatory properties, plays a critical role in the immune system. In addition to its well-documented capacity to mitigate inflammation, IL-10 can unexpectedly demonstrate pro-inflammatory characteristics under specific circumstances. The presence of both aspects emphasizes the vital need to identify the IL-10-induced peptide.

Early detection of autism spectrum disorder using explainable AI and optimized teaching strategies.

Autism spectrum disorder (ASD) is defined by the deficits of social relating, language, object use and understanding, intelligence and learning, and verbal and nonverbal communication. Most of the individuals with ASD have genetic conditions; however, early identification and intervention reduce the use of health services and other diagnostic procedures. The varied nature of ASD is widely acknowledged, with each affected individual displaying distinct traits.

Pneumonia detection on chest X-rays from Xception-based transfer learning and logistic regression.

Pneumonia is a dangerous disease that kills millions of children and elderly patients worldwide every year. The detection of pneumonia from a chest x-ray is perpetrated by expert radiologists. The chest x-ray is cheaper and is most often used to diagnose pneumonia.

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries.

Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency.

Exploring the Potential of Microservices in Internet of Things: A Systematic Review of Security and Prospects.

With the rapid growth of Internet of Things (IoT) systems, ensuring robust security measures has become paramount. Microservices Architecture (MSA) has emerged as a promising approach for enhancing IoT systems security, yet its adoption in this context lacks comprehensive analysis. This systematic review addresses this research gap by examining the incorporation of MSA in IoT systems from 2010 to 2024.