Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning.

Resource recycling is considered necessary for sustainable development, especially in smart cities where increased urbanization and the variety of waste generated require the development of automated waste management models. The development of smart technology offers a possible alternative to traditional waste management techniques that are proving insufficient to reduce the harmful effects of trash on the environment. This paper proposes an intelligent waste classification model to enhance the classification of waste materials, focusing on the critical aspect of waste classification. The proposed model leverages the InceptionV3 deep learning architecture, augmented by multi-objective beluga whale optimization (MBWO) for hyperparameter optimization. In MBWO, sensitivity and specificity evaluation criteria are integrated linearly as the objective function to find the optimal values of the dropout period, learning rate, and batch size. A benchmark dataset, namely TrashNet is adopted to verify the proposed model's performance. By strategically integrating MBWO, the model achieves a considerable increase in accuracy and efficiency in identifying waste materials, contributing to more effective waste management strategies while encouraging sustainable waste management practices. The proposed intelligent waste classification model outperformed the state-of-the-art models with an accuracy of 97.75%, specificity of 99.55%, F1-score of 97.58%, and sensitivity of 98.88%.

A deep learning predictive model for public health concerns and hesitancy toward the COVID-19 vaccines.

Throughout the pandemic era, COVID-19 was one of the remarkable unexpected situations over the past few years, but with the decentralization and globalization of efforts and knowledge, a successful vaccine-based control strategy was efficiently designed and applied worldwide. On the other hand, excused confusion and hesitation have widely impacted public health. This paper aims to reduce COVID-19 vaccine hesitancy taking into consideration the patient's medical history. The dataset used in this study is the Vaccine Adverse Event Reporting System (VAERS) dataset which was created as a corporation between the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) to gather reported side effects that may be caused by PFIEZER, JANSSEN, and MODERNA vaccines. In this paper, a Deep Learning (DL) model has been developed to identify the relationship between a certain type of COVID-19 vaccine (i.e. PFIEZER, JANSSEN, and MODERNA) and the adverse reactions that may occur in vaccinated patients. The adverse reactions under study are the recovery condition, possibility to be hospitalized, and death status. In the first phase of the proposed model, the dataset has been pre-proceesed, while in the second phase, the Pigeon swarm optimization algorithm is used to optimally select the most promising features that affect the performance of the proposed model. The patient's status after vaccination dataset is grouped into three target classes (Death, Hospitalized, and Recovered). In the third phase, Recurrent Neural Network (RNN) is implemented for both each vaccine type and each target class. The results show that the proposed model gives the highest accuracy scores which are 96.031% for the Death target class in the case of PFIEZER vaccination. While in JANSSEN vaccination, the Hospitalized target class has shown the highest performance with an accuracy of 94.7%. Finally, the model has the best performance for the Recovered target class in MODERNA vaccination with an accuracy of 97.794%. Based on the accuracy and the Wilcoxon Signed Rank test, we can conclude that the proposed model is promising for identifying the relationship between the side effects of COVID-19 vaccines and the patient's status after vaccination. The study displayed that certain side effects were increased in patients according to the type of COVID-19 vaccines. Side effects related to CNS and hemopoietic systems demonstrated high values in all studied COVID-19 vaccines. In the frame of precision medicine, these findings can support the medical staff to select the best COVID-19 vaccine based on the medical history of the patient.

Pattern and predictors of death from aluminum and zinc phosphide poisoning using multi-kernel optimized relevance vector machine.

The use of metal phosphides, particularly aluminum phosphide, poses a significant threat to human safety and results in high mortality rates. This study aimed to determine mortality patterns and predictive factors for acute zinc and aluminum phosphide poisoning cases that were admitted to Menoufia University Poison and Dependence Control Center from 2017 to 2021. Statistical analysis revealed that poisoning was more common among females (59.7%), aged between 10 and 20 years, and from rural regions. Most cases were students, and most poisonings were the result of suicidal intentions (78.6%). A new hybrid model named Bayesian Optimization-Relevance Vector Machine (BO-RVM) was proposed to forecast fatal poisoning. The model achieved an overall accuracy of 97%, with high positive predictive value (PPV) and negative predictive value (NPV) values of 100% and 96%, respectively. The sensitivity was 89.3%, while the specificity was 100%. The F1 score was 94.3%, indicating a good balance between precision and recall. These results suggest that the model performs well in identifying both positive and negative cases. Additionally, the BO-RVM model has a fast and accurate processing time of 379.9595 s, making it a promising tool for various applications. The study underscores the need for public health policies to restrict the availability and use of phosphides in Egypt and adopt effective treatment methods for phosphide-poisoned patients. Clinical suspicion, positive silver nitrate test for phosphine, and analysis of cholinesterase levels are useful in diagnosing metal phosphide poisoning, which can cause various symptoms.

A pre-trained convolutional neural network with optimized capsule networks for chest X-rays COVID-19 diagnosis.

Coronavirus disease (COVID-19) is rapidly spreading worldwide. Recent studies show that radiological images contain accurate data for detecting the coronavirus. This paper proposes a pre-trained convolutional neural network (VGG16) with Capsule Neural Networks (CapsNet) to detect COVID-19 with unbalanced data sets. The CapsNet is proposed due to its ability to define features such as perspective, orientation, and size. Synthetic Minority Over-sampling Technique (SMOTE) was employed to ensure that new samples were generated close to the sample center, avoiding the production of outliers or changes in data distribution. As the results may change by changing capsule network parameters (Capsule dimensionality and routing number), the Gaussian optimization method has been used to optimize these parameters. Four experiments have been done, (1) CapsNet with the unbalanced data sets, (2) CapsNet with balanced data sets based on class weight, (3) CapsNet with balanced data sets based on SMOTE, and (4) CapsNet hyperparameters optimization with balanced data sets based on SMOTE. The performance has improved and achieved an accuracy rate of 96.58% and an F1- score of 97.08%, a competitive optimized model compared to other related models.

Rough sets and social ski-driver optimization for drug toxicity analysis.

BACKGROUND AND OBJECTIVES: Toxicity testing is an important step for developing new drugs, and animals are widely used in this step by exposing them to the toxicants. Zebrafishes are widely used for measuring and detecting the toxicity. However, measuring and testing toxicity manually is not feasible due to the large number of embryos. This work presents an automated model to investigate the toxicity of two toxicants (3, 4-Dichloroaniline (34DCA) and p-Tert-Butylphenol (PTBP)). METHODS: The proposed model consists of two steps. In the first step, a set of features is extracted from microscopic images of zebrafish embryos using the Segmentation-Based Fractal Texture Analysis (SFTA) technique. Secondly, a novel rough set-based model using Social ski-driver (SSD) is used to find a global minimal subset of features that preserves important information of the original features. In the third step, the AdaBoost classifier is used to classify an unknown sample to alive or coagulant after exposing the embryo to a toxic compound. RESULTS: For detecting the toxicity, the proposed model is compared with (i) three deterministic rough set reduction algorithms and (ii) the PSO-based algorithm. The classification performance rate of our model was ranged from 97.1% to 99.5% and it outperformed the other algorithms. CONCLUSIONS: The results of our experiments proved that the proposed drug toxicity model is efficient for rough set-based feature selection and it obtains a high classification performance.

Effects of the dietary inclusion of a probiotic or prebiotic on florfenicol pharmacokinetic profile in broiler chicken.

We evaluated the effect of prebiotic or probiotic as feed additives on florfenicol kinetic in broilers feed. Unsexed two hundred, thirty-five-day-old broiler chickens, were put in four equal groups (n = 50). The first group was administrated florfenicol intravenous at 30 mg/kg body weight (BW) only once dosage without pre- or probiotic administration to determine the bioavailability. While, the second group was administrated florfenicol (intracrop routes; a dosage of 30 mg/kg BW for five progressive days) without pre- or probiotic co-administration. The third and the fourth groups were administrated the same dose of florfenicol (intracrop route) for five successive days, followed by 10 days of prebiotic or probiotic treatment respectively. The plasma florfenicol % was identified by high-pressure liquid chromatography (HPLC) after the first florfenicol administration (intravenous or intracrop routes) in all groups. Then, the residual levels of florfenicol were determined in liver, kidney and muscle tissues from the second, third and fourth groups which were exposed to florfenicol orally. Our results demonstrated that broilers pre-treated with prebiotic or probiotic significantly increased Cmax , AUC0- t , AUC0-inf as well as AUMC values, while significant drop was recorded in V/F and CL/F. Prebiotic or probiotic influenced the cumulative effect of florfenicol in liver and kidney tissues of treated birds.

Outcomes of Hip Arthroscopy in Patients With Previous Lumbar Spine Surgery: A Matched-Pair Controlled Comparative Study With Minimum Two-Year Follow-Up.

PURPOSE: The purpose of this study was to conduct a matched-pair analysis to determine the effect of prior lumbar spine surgery (LSS) on clinical outcomes of hip arthroscopy. METHODS: Data were prospectively collected on all patients undergoing hip arthroscopy during the study period from April 2008 to December 2012. Patients were excluded if they had previous hip conditions or had undergone prior hip surgery. Patients in the LSS group (history of LSS) were matched in a 1:1 ratio to a control group (no history of LSS) according to age ±5 years, gender, body mass index categories, Tönnis grade, and labral treatment. The following outcomes were recorded in each group: modified Harris Hip Score, Non-Arthritic Hip Score (NAHS), Hip Outcome Score-Sports Specific Subscale, Hip Outcome Score-Activities of Daily Living, and visual analogue scale (VAS) score for pain, patient satisfaction, and rates for revision hip arthroscopies and conversion to total hip arthroplasty (THA). RESULTS: During the study period, 1,405 hip arthroscopies were performed with 1,017 eligible for matching. A total of 873 (85.8%) patients had a minimum 2-year follow-up. Fifty-seven patients were matched in each group. Both groups demonstrated significant improvement in patient-reported outcome (PRO) and VAS scores. The LSS group had a lower mean for all preoperative PRO scores. There was no significant difference for the postoperative mean PRO score and change in the PRO score compared with the control group except for NAHS. The mean change in the NAHS demonstrated a greater magnitude of improvement in the LSS group. There was no significant difference between mean VAS scores, patient satisfaction, and rates for revision arthroscopy and conversion to THA between the groups. CONCLUSIONS: Prior LSS does not adversely affect outcomes of hip arthroscopy at a minimum 2-year follow-up. These patients have lower preoperative scores but similar magnitude of improvement and revision/THA rates compared with a matched comparative group of patients without prior LSS. LEVEL OF EVIDENCE: Level II, retrospective analysis of prospectively collected data.

Lectin approaches for glycoproteomics in FDA-approved cancer biomarkers.

The nine FDA-approved protein biomarkers for the diagnosis and management of cancer are approaching maturity, but their different glycosylation compositions relevant to early diagnosis still remain practically unexplored at the sub-glycoproteome scale. Lectins generally exhibit strong binding to specific sub-glycoproteome components and this property has been quite poorly addressed as the basis for the early diagnosis methods. Here, we discuss some glycoproteome issues that make tackling the glycoproteome particularly challenging in the cancer biomarkers field and include a brief view for next generation technologies.

Wearable and implantable wireless sensor network solutions for healthcare monitoring.

Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper.