Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images.

Gastrointestinal diseases cause around two million deaths globally. Wireless capsule endoscopy is a recent advancement in medical imaging, but manual diagnosis is challenging due to the large number of images generated. This has led to research into computer-assisted methodologies for diagnosing these images. Endoscopy produces thousands of frames for each patient, making manual examination difficult, laborious, and error-prone. An automated approach is essential to speed up the diagnosis process, reduce costs, and potentially save lives. This study proposes transfer learning-based efficient deep learning methods for detecting gastrointestinal disorders from multiple modalities, aiming to detect gastrointestinal diseases with superior accuracy and reduce the efforts and costs of medical experts. The Kvasir eight-class dataset was used for the experiment, where endoscopic images were preprocessed and enriched with augmentation techniques. An EfficientNet model was optimized via transfer learning and fine tuning, and the model was compared to the most widely used pre-trained deep learning models. The model's efficacy was tested on another independent endoscopic dataset to prove its robustness and reliability.

Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry.

With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.

Analyzing patients satisfaction level for medical services using twitter data.

Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer's satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.

RDET stacking classifier: a novel machine learning based approach for stroke prediction using imbalance data.

The main cause of stroke is the unexpected blockage of blood flow to the brain. The brain cells die if blood is not supplied to them, resulting in body disability. The timely identification of medical conditions ensures patients receive the necessary treatments and assistance. This early diagnosis plays a crucial role in managing symptoms effectively and enhancing the overall quality of life for individuals affected by the stroke. The research proposed an ensemble machine learning (ML) model that predicts brain stroke while reducing parameters and computational complexity. The dataset was obtained from an open-source website Kaggle and the total number of participants is 3,254. However, this dataset needs a significant class imbalance problem. To address this issue, we utilized Synthetic Minority Over-sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADAYSN), a technique for oversampling issues. The primary focus of this study centers around developing a stacking and voting approach that exhibits exceptional performance. We propose a stacking ensemble classifier that is more accurate and effective in predicting stroke disease in order to improve the classifier's performance and minimize overfitting problems. To create a final stronger classifier, the study used three tree-based ML classifiers. Hyperparameters are used to train and fine-tune the random forest (RF), decision tree (DT), and extra tree classifier (ETC), after which they were combined using a stacking classifier and a k-fold cross-validation technique. The effectiveness of this method is verified through the utilization of metrics such as accuracy, precision, recall, and F1-score. In addition, we utilized nine ML classifiers with Hyper-parameter tuning to predict the stroke and compare the effectiveness of Proposed approach with these classifiers. The experimental outcomes demonstrated the superior performance of the stacking classification method compared to other approaches. The stacking method achieved a remarkable accuracy of 100% as well as exceptional F1-score, precision, and recall score. The proposed approach demonstrates a higher rate of accurate predictions compared to previous techniques.

An Efficient Ensemble Approach for Alzheimer's Disease Detection Using an Adaptive Synthetic Technique and Deep Learning.

Alzheimer's disease is an incurable neurological disorder that leads to a gradual decline in cognitive abilities, but early detection can significantly mitigate symptoms. The automatic diagnosis of Alzheimer's disease is more important due to the shortage of expert medical staff, because it reduces the burden on medical staff and enhances the results of diagnosis. A detailed analysis of specific brain disorder tissues is required to accurately diagnose the disease via segmented magnetic resonance imaging (MRI). Several studies have used the traditional machine-learning approaches to diagnose the disease from MRI, but manual extracted features are more complex, time-consuming, and require a huge amount of involvement from expert medical staff. The traditional approach does not provide an accurate diagnosis. Deep learning has automatic extraction features and optimizes the training process. The Magnetic Resonance Imaging (MRI) Alzheimer's disease dataset consists of four classes: mild demented (896 images), moderate demented (64 images), non-demented (3200 images), and very mild demented (2240 images). The dataset is highly imbalanced. Therefore, we used the adaptive synthetic oversampling technique to address this issue. After applying this technique, the dataset was balanced. The ensemble of VGG16 and EfficientNet was used to detect Alzheimer's disease on both imbalanced and balanced datasets to validate the performance of the models. The proposed method combined the predictions of multiple models to make an ensemble model that learned complex and nuanced patterns from the data. The input and output of both models were concatenated to make an ensemble model and then added to other layers to make a more robust model. In this study, we proposed an ensemble of EfficientNet-B2 and VGG-16 to diagnose the disease at an early stage with the highest accuracy. Experiments were performed on two publicly available datasets. The experimental results showed that the proposed method achieved 97.35% accuracy and 99.64% AUC for multiclass datasets and 97.09% accuracy and 99.59% AUC for binary-class datasets. We evaluated that the proposed method was extremely efficient and provided superior performance on both datasets as compared to previous methods.

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets.

With the rise of social media platforms, sharing reviews has become a social norm in today's modern society. People check customer views on social networking sites about different fast food restaurants and food items before visiting the restaurants and ordering food. Restaurants can compete to better the quality of their offered items or services by carefully analyzing the feedback provided by customers. People tend to visit restaurants with a higher number of positive reviews. Accordingly, manually collecting feedback from customers for every product is a labor-intensive process; the same is true for sentiment analysis. To overcome this, we use sentiment analysis, which automatically extracts meaningful information from the data. Existing studies predominantly focus on machine learning models. As a consequence, the performance analysis of deep learning models is neglected primarily and of the deep ensemble models especially. To this end, this study adopts several deep ensemble models including Bi long short-term memory and gated recurrent unit (BiLSTM+GRU), LSTM+GRU, GRU+recurrent neural network (GRU+RNN), and BiLSTM+RNN models using self-collected unstructured tweets. The performance of lexicon-based methods is compared with deep ensemble models for sentiment classification. In addition, the study makes use of Latent Dirichlet Allocation (LDA) modeling for topic analysis. For experiments, the tweets for the top five fast food serving companies are collected which include KFC, Pizza Hut, McDonald's, Burger King, and Subway. Experimental results reveal that deep ensemble models yield better results than the lexicon-based approach and BiLSTM+GRU obtains the highest accuracy of 95.31% for three class problems. Topic modeling indicates that the highest number of negative sentiments are represented for Subway restaurants with high-intensity negative words. The majority of the people (49%) remain neutral regarding the choice of fast food, 31% seem to like fast food while the rest (20%) dislike fast food.

Recent Progress in Perovskite Tandem Solar Cells.

Tandem solar cells are widely considered the industry's next step in photovoltaics because of their excellent power conversion efficiency. Since halide perovskite absorber material was developed, it has been feasible to develop tandem solar cells that are more efficient. The European Solar Test Installation has verified a 32.5% efficiency for perovskite/silicon tandem solar cells. There has been an increase in the perovskite/Si tandem devices' power conversion efficiency, but it is still not as high as it might be. Their instability and difficulties in large-area realization are significant challenges in commercialization. In the first part of this overview, we set the stage by discussing the background of tandem solar cells and their development over time. Subsequently, a concise summary of recent advancements in perovskite tandem solar cells utilizing various device topologies is presented. In addition, we explore the many possible configurations of tandem module technology: the present work addresses the characteristics and efficacy of 2T monolithic and mechanically stacked four-terminal devices. Next, we explore ways to boost perovskite tandem solar cells' power conversion efficiencies. Recent advancements in the efficiency of tandem cells are described, along with the limitations that are still restricting their efficiency. Stability is also a significant hurdle in commercializing such devices, so we proposed eliminating ion migration as a cornerstone strategy for solving intrinsic instability problems.

Structural and Spectroscopic Study of New Copper(II) and Zinc(II) Complexes of Coumarin Oxyacetate Ligands and Determination of Their Antimicrobial Activity.

Tackling antimicrobial resistance is of increasing concern in a post-pandemic world where overuse of antibiotics has increased the threat of another pandemic caused by antimicrobial-resistant pathogens. Derivatives of coumarins, a naturally occurring bioactive compound, and its metal complexes have proven therapeutic potential as antimicrobial agents and in this study a series of copper(II) and zinc(II) complexes of coumarin oxyacetate ligands were synthesised and characterised by spectroscopic techniques (IR, 1H, 13C NMR, UV-Vis) and by X-ray crystallography for two of the zinc complexes. The experimental spectroscopic data were then interpreted on the basis of molecular structure modelling and subsequent spectra simulation using the density functional theory method to identify the coordination mode in solution for the metal ions in the complexes. Interestingly, the solid-state coordination environment of the zinc complexes is in good agreement with the simulated solution state, which has not been the case in our previous studies of these ligands when coordinated to silver(I). Previous studies had indicated excellent antimicrobial activity for Ag(I) analogues of these ligands and related copper and zinc complexes of coumarin-derived ligands, but in this study none of the complexes displayed antimicrobial activity against the clinically relevant methicillin-resistant Staphylococcus aureus (MRSA), Pseudomonas aeruginosa and Candida albicans.

Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network.

Pneumonia is one of the leading causes of death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on the contagious pathogen that damages the lung's tiny air sacs (alveoli). Patients with underlying disorders such as asthma, a weakened immune system, hospitalized babies, and older persons on ventilators are all at risk, particularly if pneumonia is not detected early. Despite the existing approaches for its diagnosis, low accuracy and efficiency require further research for more accurate systems. This study is a similar endeavor for the detection of pneumonia by the use of X-ray images. The dataset is preprocessed to make it suitable for transfer learning tasks. Different pre-trained convolutional neural network (CNN) variants are utilized, including VGG16, Inception-v3, and ResNet50. Ensembles are made by incorporating CNN with Inception-V3, VGG-16, and ResNet50. Besides the common evaluation metrics, the performance of the pre-trained and ensemble deep learning models is measured with Cohen's kappa as well as the area under the curve (AUC). Experimental results show that Inception-V3 with CNN attained the highest accuracy and recall score of 99.29% and 99.73%, respectively.

The Bright Side of Social Media: Social Media Platforms Adoption and Start-Up Sustainability.

This study aims to explicate the contribution of social media platforms adoption on start-up sustainability. Since most economies of the world start-up failure rate are relatively high, there is always a desire or a need to investigate the success recipe. As a result, the primary objective of this study is to understand the social media environment and how start-ups can best utilize social media platforms throughout their life cycle. Based on the qualitative case study approach, five in-depth interviews of social media marketers and individuals working in start-ups were conducted. The finding demonstrates that social media is a crucial virtual platform for striving resource start-ups. Therefore, if a platform gets utilized correctly, it can play an essential role in the sustainable progression of a start-up. Thus, there is a need for start-ups to articulate a comprehensive social media policy for each life cycle stage.

Optimization for biogenic microbial synthesis of silver nanoparticles through response surface methodology, characterization, their antimicrobial, antioxidant, and catalytic potential.

Silver is a poisonous but precious heavy metal that has widespread application in various biomedical and environmental divisions. Wide-ranging usage of the metal has twisted severe environmental apprehensions. Henceforth there is a cumulative call for the progress of modest, low-cost and, the ecological method for remediation of silver. In the present study, Bacillus cereus was isolated from contaminated soil. Various experimental factors like the amount of AgNO3, inoculum size, temperature, time, and pH were improved by using central composite design (CCD) grounded on response surface methodology (RSM). Optimized values for AgNO3 (1 mM) 10 ml, inoculum size (Bacillus cereus) 8.7 ml, temperature 48.5 °C, time 69 h, and pH 9 showed in the form of optimized ramps. The formed nanoparticles stayed characterized by UV-visible spectrophotometer, Scanning Electron Microscopy, Fourier transform infra-red spectrometry, particle size analyzer, and X-ray diffraction. The particle size ranges from 5 to 7.06 nm with spherical form. The antimicrobial effectiveness of synthesized nanoparticles was tested contrary to five multidrug resistant microbial strains, Staphylococcus epidermidis, Staphylococcus aureus, Escherichia coli, Salmonella enterica, Porteus mirabilis by disc diffusion method. The minimum inhibitory concentrations and minimum lethal concentrations were detected by the broth macro dilution method. 2,2-diphenyl-1-picrylhydrazyl-hydrate (DPPH) was used to check the free radical scavenging ability of biogenic silver nanoparticles. Similarly, anti-radical activity was checked by 2,2'-Azino-Bis-3-Ethylbenzothiazoline-6-Sulfonic Acid (ABTS) with varying time intervals. Catalytic potential of biosynthesized silver nanoparticles was also investigated.

White Light-Emitting Devices Based on Inorganic Perovskite and Organic Materials.

Perovskite-based materials have attracted considerable attention in photoelectric devices. In this paper, we report the one-step fabrication of spin-coated CsPbBr2.5I0.5 perovskite films doped with PAN (polyacrylonitrile) polymer. A red perovskite LED (PeLED) composite film was fabricated which featured a maximum luminance value of 657 cd/m² at 8 V. We fabricated white PeLEDs by combining hole transporting layer material emission, perovskite⁻polymer composite material PAN:CsPbBr2.5I0.5, and pure inorganic perovskite CsPbBr3 as a luminescent layer. The maximum luminance of the device was 360 cd/m² at 7 V, and the color coordinate was (0.31, 0.36). We obtained an ideal white light-emitting device that paves the way for further development of white PeLEDs.

Exploiting Layered Multi-Path Routing Protocols to Avoid Void Hole Regions for Reliable Data Delivery and Efficient Energy Management for IoT-Enabled Underwater WSNs.

The key concerns to enhance the lifetime of IoT-enabled Underwater Wireless Sensor Networks (IoT-UWSNs) are energy-efficiency and reliable data delivery under constrained resource. Traditional transmission approaches increase the communication overhead, which results in congestion and affect the reliable data delivery. Currently, many routing protocols have been proposed for UWSNs to ensure reliable data delivery and to conserve the node's battery with minimum communication overhead (by avoiding void holes in the network). In this paper, adaptive energy-efficient routing protocols are proposed to tackle the aforementioned problems using the Shortest Path First (SPF) with least number of active nodes strategy. These novel protocols have been developed by integrating the prominent features of Forward Layered Multi-path Power Control One (FLMPC-One) routing protocol, which uses 2-hop neighbor information, Forward Layered Multi-path Power Control Two (FLMPC-Two) routing protocol, which uses 3-hop neighbor information and 'Dijkstra' algorithm (for shortest path selection). Different Packet Sizes (PSs) with different Data Rates (DRs) are also taken into consideration to check the dynamicity of the proposed protocols. The achieved outcomes clearly validate the proposed protocols, namely: Shortest Path First using 3-hop neighbors information (SPF-Three) and Breadth First Search with Shortest Path First using 3-hop neighbors information (BFS-SPF-Three). Simulation results show the effectiveness of the proposed protocols in terms of minimum Energy Consumption (EC) and Required Packet Error Rate (RPER) with a minimum number of active nodes at the cost of affordable delay.

Novel silver(I) complexes of coumarin oxyacetate ligands and their phenanthroline adducts: Biological activity, structural and spectroscopic characterisation.

Novel silver(I) complexes of coumarin oxyacetate ligands and their phenanthroline adducts have been prepared and characterised by microanalytical data and spectroscopic techniques (IR, 1H, 13C NMR, UV-Vis). The crystal structure of one Ag(I) complex was determined by X-ray diffraction analysis. The experimental spectroscopic data have been interpreted on the basis of molecular structure modeling and subsequent spectra simulation with density functional theory method. The binding modes of the coumarins and phenanthroline ligands (monodentate, bidentate, bridging) to Ag(I) have been theoretically modelled and discussed as to the most probable ligand binding in the series of complexes studied. The antimicrobial and antifungal activities have been determined and the complexes were found to have mostly moderate antibacterial activity but some of the phenanthroline adducts were found to have antifungal activity against the clinically important fungus C. albicans, comparable to that of the commercial agents, Amphotericin B and Ketoconazole. Preliminary investigations into the possible mechanism of action of the silver complexes indicated that they did not interact with DNA via nuclease activity or intercalation but the ability to act as a superoxide dismutase mimetic may be related to their antimicrobial activity.

Characterization and application of roxithromycin loaded cyclodextrin based nanoparticles for treatment of multidrug resistant bacteria.

An outbreak of infections with a high mortality rate caused by multidrug resistant (MDR) bacteria is one of the biggest health challenges globally. A class IV drug, roxithromycin (ROX), has poor solubility. In this study, ROX was first encapsulated in the cavity of each of the ß-cyclodextrin (ßCD) and hydroxypropyl-ß-cyclodextrin (HPßCD). Then, each of the resulting ßCD-ROX inclusion complex and HPßCD-ROX inclusion complex were separately loaded into poly-(lactic-co-glycolic acid) (PLGA) to synthesize ßCD-ROX/PLGA and HPßCD-ROX/PLGA nanoparticles (NPs). Blank and ROX loaded PLGA (ROX-PLGA) NPs were also prepared. The loading efficiency of ROX is comparatively high for HPßCD-ROX/PLGA NPs in comparison to the ßCD-ROX/PLGA NPs and ROX-PLGA NPs. All designed formulations showed significant (P<0.0001) antibacterial activity against the selected MDR bacterial strains. In a nutshell, this study demonstrated a great therapeutic potential of the above-mentioned delivery systems for treatment of MDR bacteria.

Spectroscopic studies, DFT calculations, and cytotoxic activity of novel silver(I) complexes of hydroxy ortho-substituted-nitro-2H-chromen-2-one ligands and a phenanthroline adduct.

Silver(I) complexes of coumarin-based ligands and one of their phenanthroline (phen) adducts have been prepared and characterized using microanalytical data, molar conductivity, IR, (1)H and (13)C NMR, UV-Vis, and atomic absorption (AAS) spectroscopies. The binding modes of the coumarin-based ligands and the most probable structure of their Ag(I) complexes were predicted by means of molecular modeling and calculations of their IR, NMR, and absorption spectra using density functional theory (DFT). The cytotoxicity of the compounds studied against human-derived hepatic carcinoma cells (Hep-G2) and a renal cancer cell line (A498) showed that the complexes were more cytotoxic than the clinically used chemotherapeutic, mitoxantrone. The compounds showed little interaction with DNA and also did not show nuclease activity but manifested excellent superoxide dismutase activity which may indicate that their mechanism of action is quite different to many metal-based therapeutics.

Synthesis and molecular characterization of chitosan based polyurethane elastomers using aromatic diisocyanate.

The present research work was performed to synthesize a new series of chitosan based polyurethane elastomers (PUEs) using poly(ɛ-caprolactone) (PCL). The chitosan based PUEs were prepared by step-growth polymerization technique using poly(ɛ-caprolactone) (PCL) and 2,4-toluene diisocyanate (TDI). In the second step the PU prepolymer was extended with different mole ratios of chitosan and 1,4-butane diol (BDO). Molecular engineering was carried out during the synthesis. The conventional spectroscopic characterization of the synthesized samples using FT-IR confirms the existence of the proposed chitosan based PUEs structure. Internal morphology of the prepared PUEs was studied using SEM analysis. The SEM images confirmed the incorporation of chitosan molecules into the PU backbone.