A robust self-supervised approach for fine-grained crack detection in concrete structures.

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods.

Impact of low-oxalate diet on hyperoxaluria among patients suffering from nephrolithiasis.

Low-oxalate diets are useful for treating hyperoxaluria in nephrolithiasis patients. This study was unique in examining how a low-oxalate diet in addition to a standard diet affected hyperoxaluria and renal function tests in nephrolithiasis patients. The effects of a low-oxalate diet were analyzed by different biochemical tests, that is, anthropometric measurements, blood oxalate test, renal function test, electrolyte profile test, and 24 h urine analysis. For this purpose, 112 patients were divided into 2 groups: Group T1 (Conventional diet) and Group T2 (Low-Oxalate diet) for 8 weeks. Each group was tested at the initiation and end of the study. Using SPSS, the obtained data from each parameter were statistically analyzed. The results showed that a low-oxalate diet had a positive effect on patients suffering from nephrolithiasis. Furthermore, after treatment, anthropometric measurement weight (kg) among the control group (T1) was 100.45 ± 5.65 and the treatment group (T2) was 79.71 ± 9.48 kg. The effect of low-oxalate diet on renal function test: creatinine (g/d) among T1 was 2.08 ± 0.86 and T2 was 1.17 ± 0.13, uric acid(mg/d) among T1 was 437.04 ± 24.20 and T2 was 364.61 ± 35.99, urinary oxalate (mg/d) among T1 was 76.84 ± 10.33 and T2 was 39.24 ± 1.51, respectively. Sodium (mEq/d) among T1 was 156.72 ± 6.37 and T2 was 159.84 ± 6.31, potassium (mEq/d) among T1 was 69.91 ± 15.37 and T2 was 89.21 ± 6.31, phosphorus (g/d) among T1 was 0.96 ± 0.07 and T2 was 0.34 ± 0.27, respectively. This study demonstrated that nephrolithiasis patients with hyperoxaluria benefit from low-oxalate diets. Hyperoxaluria patients should eat a low-oxalate diet to use oxalate without affecting metabolism and eliminate it from the kidney without stones.

Garden cress seeds: a review on nutritional composition, therapeutic potential, and industrial utilization.

The growing preference for natural remedies has resulted in increased use of medicinal plants. One of the most significant and varied plants is garden cress (Lepidium sativum), which has large concentrations of proteins, fatty acids, minerals, and vitamins. It also contains a wide range of bioactive components, including kaempferol glucuronide, gallic acid, protocatechuic acid, coumaric acid, caffeic acid, terpenes, glucosinolates, and many more. These substances, which include antioxidant, thermogenic, depurative, ophthalmic, antiscorbutic, antianemic, diuretic, tonic, laxative, galactogogue, aphrodisiac, rubefacient, and emmengogue qualities, add to the medicinal and functional potential of garden cress. An extensive summary of the phytochemical profile and biological activity of garden cress seeds is the main goal of this review. Research showed that garden cress is one of the world's most underutilized crops, even with its nutritional and functional profile. Consequently, the goal of this review is to highlight the chemical and nutritional makeup of Lepidium sativum while paying particular attention to its bioactive profile, various health claims, therapeutic benefits, and industrial applications.

Detection of Parkinson disease using multiclass machine learning approach.

Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80-20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson's Disease.

Piperine-coated zinc oxide nanoparticles target biofilms and induce oral cancer apoptosis via BCl-2/BAX/P53 pathway.

BACKGROUND: Dental pathogens play a crucial role in oral health issues, including tooth decay, gum disease, and oral infections, and recent research suggests a link between these pathogens and oral cancer initiation and progression. Innovative therapeutic approaches are needed due to antibiotic resistance concerns and treatment limitations. METHODS: We synthesized and analyzed piperine-coated zinc oxide nanoparticles (ZnO-PIP NPs) using UV spectroscopy, SEM, XRD, FTIR, and EDAX. Antioxidant and antimicrobial effectiveness were evaluated through DPPH, ABTS, and MIC assays, while the anticancer properties were assessed on KB oral squamous carcinoma cells. RESULTS: ZnO-PIP NPs exhibited significant antioxidant activity and a MIC of 50 µg/mL against dental pathogens, indicating strong antimicrobial properties. Interaction analysis revealed high binding affinity with dental pathogens. ZnO-PIP NPs showed dose-dependent anticancer activity on KB cells, upregulating apoptotic genes BCL2, BAX, and P53. CONCLUSIONS: This approach offers a multifaceted solution to combatting both oral infections and cancer, showcasing their potential for significant advancement in oral healthcare. It is essential to acknowledge potential limitations and challenges associated with the use of ZnO NPs in clinical applications. These may include concerns regarding nanoparticle toxicity, biocompatibility, and long-term safety. Further research and rigorous testing are warranted to address these issues and ensure the safe and effective translation of ZnO-PIP NPs into clinical practice.

Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications.

The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.

Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach.

Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists' expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data. However, the precise impact of different attention mechanisms, such as channel-wise, spatial, or combined attention within the Channel-wise Attention Mode (CWAM), for brain tumor classification remains relatively unexplored. This study aims to address this gap by leveraging the power of ResNet101 coupled with CWAM (ResNet101-CWAM) for brain tumor classification. The results show that ResNet101-CWAM surpassed conventional deep learning classification methods like ConvNet, achieving exceptional performance metrics of 99.83% accuracy, 99.21% recall, 99.01% precision, 99.27% F1-score and 99.16% AUC on the same dataset. This enhanced capability holds significant implications for clinical decision-making, as accurate and efficient brain tumor classification is crucial for guiding treatment strategies and improving patient outcomes. Integrating ResNet101-CWAM into existing brain classification software platforms is a crucial step towards enhancing diagnostic accuracy and streamlining clinical workflows for physicians.

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

Effect of TiO2 Nanofillers on the Mechanical and Abrasive Wear Properties of Epoxy Reinforced with Carbon Fabric Hybrid Composites.

Recent studies show that nanofillers greatly contribute to the increase in the mechanical and abrasive behaviors of the polymer composite. In the current study, epoxy composites were made by hand lay-up with the reinforcement of carbon fabric and titanium dioxide (TiO2) nanoparticles as secondary reinforcement in weight percentages of 0.5, 1.0, and 2.0. Hardness, tensile, and abrasive wear tests have been carried out for the fabricated composites. The obtained results confirm that as the percentage of filler addition increases, hardness of the carbon epoxy (CE) composite increases, and significant enhancement of 10.25% hardness is confirmed in 2 wt % nano TiO2-added CE composite. The CE composite filled with 2 wt % of TiO2 nanofiller shows 15.77 and 9.15% improvement of tensile strength and modulus, respectively, compared to unfilled CE composites. The abrasive wear volume exhibits a nearly linear increasing trend as the abrading distance increases. In addition, it is discovered that the abrasive wear volume is greater for higher applied loads. The inclusion of nano TiO2 reduced the wear loss in the CE composite for all abrading distances, regardless of the load, low or high. The scanning electron microscopy analysis of worn surfaces was carried out to analyze the contribution of the filler to improve the wear resistance.

Novel design of cryptographic architecture of nanorouter using quantum-dot cellular automata nanotechnology.

The article introduces a revolutionary Nanorouter structure, which is a crucial component in the Nano communication regime. To complete the connection, many key properties of Nanorouters are investigated and merged. QCA circuits with better speed and reduced power dissipation aid in meeting internet standards. Cryptography based on QCA design methodologies is a novel concept in digital circuit design. Data security in nano-communication is crucial in data transmission and reception; hence, cryptographic approaches are necessary. The data entering the input line is encrypted by an encoder, and then sent to the designated output line, where it is decoded and transferred. The Nanorouter is offered as a data path selector, and the proposed study analyses the cell count of QCA and the circuit delay. In this manuscript, novel designs of (4:1)) Mux and (1:4) Demux designs are utilized to implement the proposed nanorouter design. The proposed (4:1) Mux design requires 3-5% fewer cell counts and 20-25% fewer area, and the propsoed (1:4) Demux designs require 75-80% fewer cell counts and 90-95% fewer area compared to their latest counterparts. The QCAPro utility is used to analyse the power consumption of several components that make up the router. QCADesigner 2.0.3 is used to validate the simulation results and output validity.

Optimizing fault tolerance of RAM cell through MUX based modeling and design using symmetries of QCA cells.

Extensive research is now being conducted on the design and construction of logic circuits utilizing quantum-dot cellular automata (QCA) technology. This area of study is of great interest due to the inherent advantages it offers, such as its compact size, high speed, low power dissipation, and enhanced switching frequency in the nanoscale domain. This work presents a design of a highly efficient RAM cell in QCA, utilizing a combination of a 3-input and 5-input Majority Voter (MV) gate, together with a 2 × 1 Multiplexer (MUX). The proposed design is also investigated for various faults such as single cell deletion, single cell addition and single cell displacement or misalignment defects. The circuit under consideration has a high degree of fault tolerance. The functionality of the suggested design is showcased and verified through the utilization of the QCADesigner tool. Based on the observed performance correlation, it is evident that the proposed design demonstrates effectiveness in terms of cell count, area, and latency. Furthermore, it achieves a notable improvement of up to 76.72% compared to the present configuration in terms of quantum cost. The analysis of energy dissipation, conducted using the QCAPro tool, is also shown for various scenarios. It is seen that this design exhibits the lowest energy dispersion, hence enabling the development of ultra-low power designs for diverse microprocessors and microcontrollers.

Trained neural networking framework based skin cancer diagnosis and categorization using grey wolf optimization.

Skin Cancer is caused due to the mutational differences in epidermis hormones and patch appearances. Many studies are focused on the design and development of effective approaches in diagnosis and categorization of skin cancer. The decisions are made on independent training dataset under limited editions and scenarios. In this research, the kaggle based datasets are optimized and categorized into a labeled data array towards indexing using Federated learning (FL). The technique is developed on grey wolf optimization algorithm to assure the dataset attribute dependencies are extracted and dimensional mapping is processed. The threshold value validation of the dimensional mapping datasets is effectively optimized and trained under the neural networking framework further expanded via federated learning standards. The technique has demonstrated 95.82% accuracy under GWO technique and 94.9% on inter-combination of Trained Neural Networking (TNN) framework and Recessive Learning (RL) in accuracy.

Exploring fetal brain tumor glioblastoma symptom verification with self organizing maps and vulnerability data analysis.

Brain tumor glioblastoma is a disease that is caused for a child who has abnormal cells in the brain, which is found using MRI "Magnetic Resonance Imaging" brain image using a powerful magnetic field, radio waves, and a computer to produce detailed images of the body's internal structures it is a standard diagnostic tool for a wide range of medical conditions, from detecting brain and spinal cord injuries to identifying tumors and also in evaluating joint problems. This is treatable, and by enabling the factor for happening, the factor for dissolving the dead tissues. If the brain tumor glioblastoma is untreated, the child will go to death; to avoid this, the child has to treat the brain problem using the scan of MRI images. Using the neural network, brain-related difficulties have to be resolved. It is identified to make the diagnosis of glioblastoma. This research deals with the techniques of max rationalizing and min rationalizing images, and the method of boosted division time attribute extraction has been involved in diagnosing glioblastoma. The process of maximum and min rationalization is used to recognize the Brain tumor glioblastoma in the brain images for treatment efficiency. The image segment is created for image recognition. The method of boosted division time attribute extraction is used in image recognition with the help of MRI for image extraction. The proposed boosted division time attribute extraction method helps to recognize the fetal images and find Brain tumor glioblastoma with feasible accuracy using image rationalization against the brain tumor glioblastoma diagnosis. In addition, 45% of adults are affected by the tumor, 40% of children and 5% are in death situations. To reduce this ratio, in this study, the Brain tumor glioblastoma is identified and segmented to recognize the fetal images and find the Brain tumor glioblastoma diagnosis. Then the tumor grades were analyzed using the efficient method for the imaging MRI with the diagnosis result of partially high. The accuracy of the proposed TAE-PIS system is 98.12% which is higher when compared to other methods like Genetic algorithm, Convolution neural network, fuzzy-based minimum and maximum neural network and kernel-based support vector machine respectively. Experimental results show that the proposed method archives rate of 98.12% accuracy with low response time and compared with the Genetic algorithm (GA), Convolutional Neural Network (CNN), fuzzy-based minimum and maximum neural network (Fuzzy min-max NN), and kernel-based support vector machine. Specifically, the proposed method achieves a substantial improvement of 80.82%, 82.13%, 85.61%, and 87.03% compared to GA, CNN, Fuzzy min-max NN, and kernel-based support vector machine, respectively.

Royal Poinciana Biodiesel Blends with 1-Butanol as a Potential Alternative Fuel for Unmodified Research Engines.

This research work investigates the experimental work of a single-cylinder diesel engine operated with royal poinciana biodiesel blends with various proportions of 10, 20, and 30% volume with 1-butanol as an effective ignition-improving additive. The test blends were indicated as D90RP7B3 (90% diesel + 7% royal poinciana biodiesel + 3% butanol), D80RP14B6 (80% diesel + 14% royal poinciana biodiesel + 6% butanol), D70RP21B9 (70% diesel + 21% royal poinciana biodiesel + 9% butanol), and pure royal poinciana biodiesel (RP100) and diesel. The significant findings or results obtained during the experimentation are that BTE is suitable for blend D90RP7B3, and the least BSFC is found for blend D90RP7B3 in the 0.24 kg/kWh range. The inline cylinder pressures are found to be suitable for the blend D90RP7B3 in the range of 7 MPa; HRR is ideal for both the blends D90RP7B3 and D80RP14B6 in the range of 90 and 88 kJ; D90RP7B3 possesses adequate ignition delay at full load conditions 16° in crank angle advance; maximum A/F ratios are well suitable for the blend D90RP7B3 in the ratio 11:1 at higher loads. Volumetric efficiency is achieved well for all the blends and diesel; the emissions released from the royal poinciana blends, such as CO, CO2, HC, and NOX, were reduced by 14.12, 8.33, 11.1, and 18.8% compared to standard diesel. Hence, royal poinciana blends with 1-butanol can be considered the best fuels in the automobile sector.

Hybrid similarity relation based mutual information for feature selection in intuitionistic fuzzy rough framework and its applications.

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

Recent Advances in Graphene-Enabled Materials for Photovoltaic Applications: A Comprehensive Review.

Graphene's two-dimensional structural arrangement has sparked a revolutionary transformation in the domain of conductive transparent devices, presenting a unique opportunity in the renewable energy sector. This comprehensive Review critically evaluates the most recent advances in graphene production and its employment in solar cells, focusing on dye-sensitized, organic, and perovskite devices for bulk heterojunction (BHJ) designs. This comprehensive investigation discovered the following captivating results: graphene integration resulted in a notable 20.3% improvement in energy conversion rates in graphene-perovskite photovoltaic cells. In comparison, BHJ cells saw a laudable 10% boost. Notably, graphene's 2D internal architecture emerges as a protector for photovoltaic devices, guaranteeing long-term stability against various environmental challenges. It acts as a transportation facilitator and charge extractor to the electrodes in photovoltaic cells. Additionally, this Review investigates current research highlighting the role of graphene derivatives and their products in solar PV systems, illuminating the way forward. The study elaborates on the complexities, challenges, and promising prospects underlying the use of graphene, revealing its reflective implications for the future of solar photovoltaic applications.

Employing deep learning and transfer learning for accurate brain tumor detection.

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.

Entropy for q-rung linear diophantine fuzzy hypersoft set with its application in MADM.

A notable advancement in fuzzy set theory is the q-rung linear diophantine fuzzy set. The soft set theory was expanded into the hypersoft set theory. By combining both the q-rung linear diophantine fuzzy set and hypersoft set, this study describes the notion of q-rung linear diophantine fuzzy hypersoft set that can handle multi sub-attributed q-rung linear diophantine fuzzy situations in the real world. Furthermore, some of its algebraic operations such as union, intersection and complement are described in this study. In addtion, the entropy measure of the q-rung linear diophantine fuzzy hypersoft set is established as it is helpful in determining the degree of fuzziness of q-rung linear diophantine fuzzy hypersoft sets. A multi-attribute decision making algorithm based on suggested entropy is presented in this study along with a numerical example of selecting a suitable wastewater treatment technology to demonstrate the effectiveness of the proposed algorithm in real-life situations. A comparative study was undertaken that describes the validity, robustness and superiority of the proposed algorithm and notions by discussing the advantages and drawbacks of existing theories and algorithms. Overall, this study describes a novel fuzzy extension that prevails over the existing ones and contributes to the real world with a valid real-life multi-attribute decision making algorithm that can cover many real-world problems that are unable to be addressed by the existing methodology.

Predictive healthcare modeling for early pandemic assessment leveraging deep auto regressor neural prophet.

In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP's efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases.