Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification.

Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques.

Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment.

Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively.

Bio-Inspired Spotted Hyena Optimizer with Deep Convolutional Neural Network-Based Automated Food Image Classification.

Food image classification, an interesting subdomain of Computer Vision (CV) technology, focuses on the automatic classification of food items represented through images. This technology has gained immense attention in recent years thanks to its widespread applications spanning dietary monitoring and nutrition studies to restaurant recommendation systems. By leveraging the developments in Deep-Learning (DL) techniques, especially the Convolutional Neural Network (CNN), food image classification has been developed as an effective process for interacting with and understanding the nuances of the culinary world. The deep CNN-based automated food image classification method is a technology that utilizes DL approaches, particularly CNNs, for the automatic categorization and classification of the images of distinct kinds of foods. The current research article develops a Bio-Inspired Spotted Hyena Optimizer with a Deep Convolutional Neural Network-based Automated Food Image Classification (SHODCNN-FIC) approach. The main objective of the SHODCNN-FIC method is to recognize and classify food images into distinct types. The presented SHODCNN-FIC technique exploits the DL model with a hyperparameter tuning approach for the classification of food images. To accomplish this objective, the SHODCNN-FIC method exploits the DCNN-based Xception model to derive the feature vectors. Furthermore, the SHODCNN-FIC technique uses the SHO algorithm for optimal hyperparameter selection of the Xception model. The SHODCNN-FIC technique uses the Extreme Learning Machine (ELM) model for the detection and classification of food images. A detailed set of experiments was conducted to demonstrate the better food image classification performance of the proposed SHODCNN-FIC technique. The wide range of simulation outcomes confirmed the superior performance of the SHODCNN-FIC method over other DL models.

Adult Patient Satisfaction with Nursing Care Services and Associated Factors Among Admitted Patients at Saint Paul's Hospital, Millennium Medical College, Addis Ababa, Ethiopia, 2022: A Cross-Sectional Study.

BACKGROUND: Providing comprehensive nursing care and ensuring patient satisfaction are essential health performance indicators worldwide. Despite some efforts to improve patient satisfaction with nursing care, the approach in developing countries, including Ethiopia, remains insufficient. This study aimed to assess the level of adult patient satisfaction and identify the factors affecting satisfaction. MATERIALS AND METHODS: This cross-sectional study included 407 participants selected using a simple randomization technique. The samples were distributed using proportional allocation to each selected adult inpatient department. The participants were interviewed using a modified structured Amharic version of the Newcastle Satisfaction with Nursing Scale. Bivariate and multivariable logistic regression analyses were also performed. RESULTS: The overall level of patient satisfaction with nursing care services was 54.3%. Respondents without formal education (P=0.010), male sex (P=0.041), free service consumers (P0.001), and health insurance users (P0.001) were significantly associated with satisfaction with nursing care. In addition, previously hospitalized patients (P=0.001), governmental workers (P0.001), and patients admitted to the medical ward (P=0.010) were associated with patient dissatisfaction with nursing care services. CONCLUSION: This study revealed that adult patient satisfaction with nursing care services is low. A previous admission history, higher education level, paying cash for services, and private and governmental workers were significant predisposing factors for dissatisfaction with nursing care. On the other hand, patients without formal education, free-service consumers, and male sex were significant predictors of satisfaction with nursing care services. Therefore, hospital administrators are encouraged to focus on patients' needs and expectations.

Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries.

Customer churn prediction (CCP) implies the deployment of data analytics and machine learning (ML) tools to forecast the churning customers, i.e., probable customers who may remove their subscriptions, thus allowing the companies to apply targeted customer retention approaches and reduce the customer attrition rate. This predictive methodology improves active customer management and provides enriched satisfaction to the customers and also continuous business profits. By recognizing and prioritizing the relevant features, such as usage patterns and customer collaborations, and also by leveraging the capability of deep learning (DL) algorithms, the telecom companies can develop highly robust predictive models that can efficiently anticipate and mitigate customer churn by boosting retention approaches. In this background, the current study presents the Archimedes optimization algorithm-based feature selection with a hybrid deep-learning-based churn prediction (AOAFS-HDLCP) technique for telecom companies. In order to mitigate high-dimensionality problems, the AOAFS-HDLCP technique involves the AOAFS approach to optimally choose a set of features. In addition to this, the convolutional neural network with autoencoder (CNN-AE) model is also involved for the churn prediction process. Finally, the thermal equilibrium optimization (TEO) technique is employed for hyperparameter selection of the CNN-AE algorithm, which, in turn, helps in achieving improved classification performance. A widespread experimental analysis was conducted to illustrate the enhanced performance of the AOAFS-HDLCP algorithm. The experimental outcomes portray the high efficiency of the AOAFS-HDLCP approach over other techniques, with a maximum accuracy of 94.65%.

Prevalence and assessment of biochemical parameters of attention-deficit hyperactivity disorder children in Bangladesh.

BACKGROUND AND OBJECTIVES: This study aimed to evaluate some new biochemical parameters that help ensuring the early and precise diagnosis of attention-deficit hyperactivity disorder (ADHD) in blood plasma. DESIGN AND SETTINGS: A prospective study conducted with patients scheduled for some new biochemical parameters that help ensuring the early and precise diagnosis of ADHD in blood plasma in a Child Development Center of the Chittagong, Bangladesh. MATERIALS AND METHODS: The study was carried out at two levels. The first level was questionnaire on personal data and disease history while the second was on biochemical examination of the plasma ammonia and lactate status. A total of 100 children (age range 2 years 4 months to 12 years 6 months, mean age 7 years 5 months) were investigated in this study among 75 were male and 25 were female. This study was conducted in Chittagong Maa-O-Shishu General Hospital, Bangladesh. RESULTS: We observed that the level of plasma ammonia and lactate were higher in ADHD children (36-60 µmol/L; P < 0.05 and 22-30 µmol/L; P < 0.05, respectively) compare to a reference value. The prevalence of ADHD is higher in male (75%) than in female (25%) with a ratio of 3:1. Consanguinity increases the risk of having ADHD in the next generation. CONCLUSION: This study concludes that there might be a correlation between ADHD and increased level of plasma ammonia and lactate level, and those might be an important parameter in the diagnosis of ADHD patients.