Eco-Friendly Extraction: A green approach to maximizing bioactive extraction from pumpkin (Curcubita moschata L.)

The research focused on optimizing the accelerated solvent extraction (ASE) of carotenoids and polyphenols from pumpkin powder. The study optimized accelerated solvent extraction (ASE) of carotenoids and polyphenols from pumpkin powder. Using a mix of standard score (SS) and artificial neural network (ANN) methods, the extraction process was fine-tuned. The ANN model assessed extraction parameters' significance, achieving high predictability for total carotenoid content (TCC), total phenolic content (TPC), and free radical scavenging capacity (DPPH and ABTS methods). The analysis highlighted the most effective extraction at 50 % concentration, 120 °C temperature, 5 min duration, and 2 cycles, yielding high carotenoid and phenolic content (TCC 571.49 µg/g, TPC 7.85 mg GAE/g). HPLC-DAD profiles of the optimized ASE extract confirmed major carotenoids and phenolic compounds. Strong correlations were found between bioactive compounds and antioxidant activity, emphasizing potential health benefits.

New insights into antibubble formation by single drop impact on a same-liquid pool

HYPOTHESIS

Secondary drops (SDs) generated when falling drops impact a same-liquid bath can potentially generate antibubbles. Different mechanisms of antibubble formation can be identified and their size and formation probability (PAb) can be predicted.

EXPERIMENTS

Surfactant solutions were dropped from various heights using a highly stable pulseless microfluidic pump in a same-liquid bath. The impact was recorded using a high-speed camera. The formation of SDs and antibubbles as well as their sizes were evaluated considering the falling-drop height (HFD) and dimensionless parameters.

FINDINGS

This study reports new mechanisms for antibubble formation from SDs. A decrease in the surface tension yielded a thinner central jet, thereby yielding more SDs. Larger values of the HFD, impact velocity (U), and Weber number (We) increased the SD size and decreased the SD count; the increase in size increased the antibubble size. The number of SDs correlated with the formation of two distinct antibubbles or a single (coalesced) antibubble. The plots for PAb versus HFD, U, and We exhibited two distinct peaks. A moderate increase in the surfactant concentration enhanced PAb in the first regime, whereas an excessive concentration limited antibubble formation. Artificial neural modeling can successfully predict antibubble formation. These findings provide valuable insights for the research on controlled antibubble generation.

Evaluating embodied energy, carbon impact, and predictive precision through machine learning for pavers manufactured with treated recycled construction and demolition waste aggregate

This investigation assesses the embodied energy and carbon footprint in the manufacture of pavers using varying proportions of recycled Construction and Demolition Waste (CDW). Additionally, Thin Film Composite Polyamide fiber (TFC PA), extracted from end-of-life Reverse Osmosis (RO) membranes, is introduced as an additive to enhance the concrete's strength. Machine learning techniques, namely Artificial Neural Network (ANN), Support Vector Regression (SVR), and Response Surface Methodology (RSM), are employed to predict the mechanical properties of pavers. The study focuses on examining the energy required and embodied carbon in various mix proportions, as well as the mechanical properties-specifically compressive strength and split tensile strength of concrete with different CDW and TFC PA proportions. Findings reveal that the optimal percentage of TFC PA is 3 % for all CDW replacement proportions, resulting in low carbon content both in terms of energy and embodiment and in mechanical behavior. The implementation of ANN and SVR is conducted in MATLAB, while a Design Expert is employed to generate the experimental design for RSM. The RSM regression model demonstrates a robust correlation between variables and observed outcomes, with optimal p-values, R2 values, and f-values. The ANN model successfully captures the variability in the data. Additionally, the findings indicate a consistent superiority of the Support Vector Regression (SVR) model over both Artificial Neural Network (ANN) and Response Surface Model (RSM) models when considering diverse performance metrics such as residuals and correlation coefficients.

Optimization of α-L-arabinofuranosidase CcABF on clarification and beneficial active substances in fermented ginkgo kernel juice by artificial neural network and genetic algorithm

This study aimed at using α-L-arabinofuranosidase CcABF to improve the clarity and active substances in fermented ginkgo kernel juice by artificial neural network (ANN) modeling and genetic algorithm (GA) optimization. A credible three-layer feedforward ANN model was established to predict the optimal parameters for CcABF clarification. The experiments proved the highest transmittance of 89.40% for fermented ginkgo kernel juice with this understanding, which exhibited a 25.56% increase over the unclarified group. With the clarification of CcABF, the antioxidant capacity in juice was enhanced with the increase of total phenolic and flavone contents, and the maximum DPPH and hydroxyl radical scavenging rates were increased by 89.71% and 26.65%, respectively. The contents of toxic ginkgolic acids declined markedly, while the active ingredients of ginkgetin and ginkgolide B showed a modest increase. Moreover, changes in free amino acids and volatile compounds improved the nutritive value and flavor of clarified fermented ginkgo kernel juice.

Zonal demand-controlled ventilation strategy to minimize infection probability and energy consumption: A coordinated control based on occupant detection

Due to the outbreak of COVID-19, an increased risk of airborne transmission has been experienced in buildings, particularly in confined public places. The need for ventilation as a means of infection prevention has become more pronounced given that some basic precautions (like wearing masks) are no longer mandatory. However, ventilating the space as a whole (e.g., using a unified ventilation rate) may lead to situations where there is either insufficient or excessive ventilation in localized areas, potentially resulting in localized virus accumulation or large energy consumption. It is of urgent need to investigate real-time control of ventilation systems based on local demands of the occupants to strike a balance between infection risk and energy saving. In this work, a zonal demand-controlled ventilation (ZDCV) strategy was proposed to optimize the ventilation rates in sub-zones. A camera-based occupant detection method was developed to detect occupants (with eight possible locations in sub-zones denoted as 'A' to 'H'). Linear ventilation model (LVM), dimension reduction, and artificial neural network (ANN) were integrated for rapid prediction of pollutant concentrations in sub-zones with the identified occupants and ventilation rates as inputs. Coordinated ventilation effects between sub-zones were optimized to improve infection prevention and energy savings. Results showed that rapid prediction models achieved an average prediction error of 6 ppm for CO2 concentration fields compared with the simulation under different occupant scenarios (i.e., occupant locations at ABH, ABCFH, and ABCDEFH). ZDCV largely reduced the infection risk to 2.8% while improved energy-saving efficiency by 34% compared with the system using constant ventilation rate. This work can contribute to the development of building environmental control systems in terms of pollutant removal, infection prevention, and energy sustainability.

A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm

Cybersecurity is critical in today's digitally linked and networked society. There is no way to overestimate the importance of cyber security as technology develops and becomes more pervasive in our daily lives. Cybersecurity is essential to people's protection. One type of cyberattack known as "credential stuffing" involves using previously acquired usernames and passwords by attackers to access user accounts on several websites without authorization. This is feasible as a lot of people use the same passwords and usernames on several different websites. Maintaining the security of online accounts requires defence against credential-stuffing attacks. The problems of credential stuffing attacks, failure detection, and prediction can be handled by the suggested EWOA-ANN model. Here, a novel optimization approach known as Enhanced Whale Optimization Algorithm (EWOA) is put on to train the neural network. The effectiveness of the suggested attack identification model has been demonstrated, and an empirical comparison will be carried out with respect to specific security analysis.

A comprehensive study of artificial neural network for sensitivity analysis and hazardous elements sorption predictions via bone char for wastewater treatment

Using bone char for contaminated wastewater treatment and soil remediation is an intriguing approach to environmental management and an environmentally friendly way of recycling waste. The bone char remediation strategy for heavy metal-polluted wastewater was primarily affected by bone char characteristics, factors of solution, and heavy metal (HM) chemistry. Therefore, the optimal parameters of HM sorption by bone char depend on the research being performed. Regarding enhancing HM immobilization by bone char, a generic strategy for determining optimal parameters and predicting outcomes is crucial. The primary objective of this research was to employ artificial neural network (ANN) technology to determine the optimal parameters via sensitivity analysis and to predict objective function through simulation. Sensitivity analysis found that for multi-metals sorption (Cd, Ni, and Zn), the order of significance for pyrolysis parameters was reaction temperature > heating rate > residence time. The primary variables for single metal sorption were solution pH, HM concentration, and pyrolysis temperature. Regarding binary sorption, the incubation parameters were evaluated in the following order: HM concentrations > solution pH > bone char mass > incubation duration. This approach can be used for further experiment design and improve the immobilization of HM by bone char for water remediation.

Lake evaporation in arid zones: Leveraging Landsat 8's water temperature retrieval and key meteorological drivers

This study assessed the accuracy of various methods for estimating lake evaporation in arid, high-wind environments, leveraging water temperature data from Landsat 8. The evaluation involved four estimation techniques: the FAO 56 radiation-based equation, the Schendel temperature-based equation, the Brockamp & Wenner mass transfer-based equation, and the VUV regression-based equation. The study focused on the Chah Nimeh Reservoirs (CNRs) in the arid region of Iran due to its distinctive wind patterns and dry climate. Our analysis revealed that the Split-window algorithm was the most precise for satellite-based water surface temperature measurement, with an R2 value of 0.86 and an RMSE of 1.61 °C. Among evaporation estimation methods, the FAO 56 stood out, demonstrating an R2 value of 0.76 and an RMSE of 4.36 mm/day in comparison to pan evaporation measurements. A subsequent sensitivity analysis using an artificial neural network (ANN) identified net radiation as the predominant factor influencing lake evaporation, especially during both wind and no-wind conditions. This research underscores the importance of incorporating net radiation, water surface temperature, and wind speed parameters in evaporation evaluations, providing pivotal insights for effective water management in arid, windy regions.

Synergistic effects of ternary mixture formulation and process parameters optimization in a sequential approach for enhanced L-asparaginase production using agro-industrial wastes

A novel ternary mixture of inexpensive and nutrient-rich agro-substrates comprising groundnut de-oiled cake, corn gluten meal, and soybean meal has been explored to enhance the L-asparaginase production in solid-state fermentation. To achieve the aim, a hybrid strategy was implemented by utilizing a combination of a mixture design and artificial neural networks. The study initiated with the judicious selection of the agro-substrates based on their low C/N content in comparison to the control using the CHNS elemental analysis. The mixture composition of soybean meal (49.0%), groundnut de-oiled cake (31.5%), and corn gluten meal (19.5%) were found optimum using the simplex lattice mixture design. The agro-industrial substrates mix revealed synergistic effects on the L-asparaginase production than either of the substrates alone. The maximum L-asparaginase activity of 141.45 ± 5.24 IU/gds was observed under the physical process conditions of 70% moisture content, autoclaving period of 30 min and 6.0 pH by adopting the machine learning-derived artificial neural network (ANN) methodology. The ANN modeling showed excellent prediction ability with a low mean squared error of 0.7, a low root mean squared error of 0.84, and a high value of 0.99 for regression coefficient. Moisture content (%) was assessed to be the most sensitive process parameter in the global sensitivity analysis. The net outcome from the two sequential optimization designs is the selection of the ideal mixture composition followed by the optimum physical process parameters. The application of the enzyme demonstrated significant cytotoxicity against leukemia cell line and therefore exhibited an anti-cancer effect. The present study reports a novel mixture combination and methodology that can be used to lower the cost and enhance the production of L-asparaginase using an agro-industrial substrate mixture.

Application of ANN-MOGA for nutrient sequestration for wastewater remediation and production of polyunsaturated fatty acid (PUFA) by Chlorella sorokiniana MSP1

Chlorella bears excellent potential in removing nutrients from industrial wastewater and lipid production enriched with polyunsaturated fatty acids. However, due to the changing nutrient dynamics of wastewater, growth and metabolic activity of Chlorella are affected. In order to sustain microalgal growth in wastewater with concomitant production of PUFA rich lipids, RSM (Response Surface Methodology) followed by heuristic hybrid computation model ANN-MOGA (Artificial Neural Network- Multi-Objective Genetic Algorithm) were implemented. Preliminary experiments conducted taking one factor at a time and design matrix of RSM with process variables viz. Sodium chloride (1 mM-40 mM), Magnesium sulphate (100 mg-800 mg) and incubation time (4th day to 20th day) were validated by ANN-MOGA. The study reported improved biomass and lipid yield by 54.25% and 12.76%, along with total nitrogen and phosphorus removal by 21.92% and 18.72% respectively using ANN-MOGA. It was evident from FAME results that there was a significantly improved concentration of linoleic acid (19.1%) and γ-linolenic acid (21.1%). Improved PUFA content makes it a potential feedstock with application in cosmeceutical, pharmaceutical and nutraceutical industry. The study further proves that C. sorokiniana MSP1 mediated industrial wastewater treatment with PUFA production is an effective way in providing environmental benefits along with value addition. Moreover, ANN-MOGA is a relevant tool that could control microalgal growth in wastewater.

Modeling and optimization of poly(lactic acid)/poly(ℇ-caprolactone)/Nigella sativa extract nanofibers production for skin wounds healing by artificial neural network and response surface methodology models

Electrospun fibrous scaffolds have great potential for the effective treatment of wounds. Novel blend scaffolds were fabricated from poly(ℇ- caprolactone) (PCL)/poly (lactic acid) (PLA) with Nigella sativa (NS) extract in different concentrations of 10 %, 15 %, 20 %, and 25 % by one nozzle electrospinning. RSM and ANN models were used to determine optimal nanofiber. The results showed that the ANN model had average goodness values of almost 1.992 which was higher than the RSM model with an amount of 1.823. The best sample was determined with the combination of parameters such as PLA/PCL (70:29) concentration, voltage 17 kV, and flow rate 0.2 ml/h in diameter of nanofiber 410 nm by Genetic Algorithm (GA) model with cost value 0.0216 that was lower than cost value (0.0927) of ANN model. The effect of NS extract on nanofibers properties showed that loading high concentrations of NS extract in PLA/PCL polymer solutions caused a decrease in nanofibers diameter, hydrophilicity, and tensile strength. Overall, PLA/PCL/NS 25 % nanofiber was selected as an optimal web with an average diameter of 370 ± 68 nm with a young modulus 5.94 MPa. This scaffold also exhibited the highest antibacterial activity, cell attachment, and cell viability based on the MTT assay.

A novel diagnosis method for schizophrenia based on globus pallidus data

This research aims to diagnose schizophrenia with machine learning-based algorithms. Bayesian neural network, logistic regression, decision tree, k-nearest neighbor, and gaussian kernel classification techniques are investigated to diagnose schizophrenia with data from 125 persons. This study showed that left lateral ventricles and left globus pallidus volumes and their percentages in the brain were significantly lower than HCs in FEP patients. Using brain volumes, we were able to diagnose FEP with an accuracy of 73.6 % via logistic regression and with an accuracy of 86.4 % using the SVM kernel classifier method. Therefore, brain volumes can be used to diagnose FEP with the SVM kernel classifier method.

Artificial intelligence: Machine learning approach for screening large database and drug discovery

Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.

Application of GIS and feedforward back-propagated ANN models for predicting the ecological and health risk of potentially toxic elements in soils in Northwestern Nigeria

Potentially toxic elements (PTEs) occur naturally in most geologic materials. However, recent anthropogenic disturbances such as ore mining have contributed significantly to their enrichment in soils. Their occurrence in soil may portend a myriad of related risks to the environment and biota. Most traditional soil quality evaluation methods involve comparing the background values of the elements to the established guideline values, which is often time-consuming and fraught with computational errors. As a result, to conduct a comprehensive and unbiased evaluation of soil quality and its effects on the ecosystem and human health, this research combined geochemical, numerical, and GIS data for a composite health risk zonation of the entire study area. Furthermore, the multilayer perceptron artificial neural network (MLP-NN) was used to forecast the most important toxic components influencing soil quality. Geochemical, statistical, and quantitative soil pollution evaluation (pollution index and ecological risk index) showed that apart from mining, the spread and association of trace elements and oxides occur as a consequence of surface environmental conditions (e.g., leaching, weathering, and organo-metallic complexation). The hazard quotients (HQs) and hazard index (HI) of all PTEs were greater than one. This indicates that residents (particularly children) are more susceptible to risks from toxic element ingestion than dermal exposure and inhalation. Ingestion of As and Cr resulted in higher cancer risks and lifetime cancer risk levels (> 1.0E 04), with risk levels increasing toward the northeastern, western, and southeastern directions of the study area. The low modeling errors observed from the sum of square errors, relative errors, and coefficient of determination confirmed the efficiency of the MLP-NN in pollution load prediction. Based on the sensitivity analysis, Hg, Sr, Zn, Ba, As, and Zr showed the greatest influence on soil quality. Focus on remediation should therefore be placed on the removal of these elements from the soil.

The combination of multiple linear regression and adaptive neuro-fuzzy inference system can accurately predict trihalomethane levels in tap water with fewer water quality parameters

Artificial Neural Network (ANN) models are accurate in predicting the levels of disinfection by-products (DBPs) in drinking water. However, these models are not yet practical due to the large number of parameters involved, which should take a significant amount of time and cost to detect. Developing accurate and reliable prediction models of DBPs with fewest parameters is essential in the management of drinking water safety. This study used the adaptive neuro-fuzzy inference system (ANFIS) and radial basis function artificial neural network (RBF-ANN) to predict the levels of trihalomethanes (THMs), the most abundant DBPs in drinking water. Two water quality parameters identified by multiple linear regression (MLR) models were used as model inputs, and the quality of the models was assessed based on criteria such as correlation coefficient (r), mean absolute relative error (MARE), and the percentage of predictions with absolute relative error less than 25% (NE<25%) and over than 40% (NE>40%), etc. The results showed that the ANFIS models had higher correlation coefficients (r = 0.853-0.898) and prediction accuracy (NE<25% = 91%-94%) compared to RBF-ANN models (r = 0.553-0.819; NE<25% = 77%-86%) and traditional MLR models (r = 0.389-0.619; NE<25% = 67%-77%). Conversely, the prediction error, as indicated by MARE and NE>40%, showed the opposite trend: ANFIS models (MARE = 8%-11%; NE>40% = 0-5%) < RBF-ANN models (MARE = 15%-18%; NE>40% = 5%-11%) < MLR models (MARE = 19%-21%; NE>40% = 11%-17%). The present study provided a novel approach for constructing high-quality prediction models of THMs in water supply systems using only two parameters. This method holds promise as a viable alternative for monitoring THMs concentrations in tap water, thereby contributing to the improvement of water quality management strategies.

Optimization of Gelatin Methacryloyl Hydrogel Properties through an Artificial Neural Network Model

Gelatin methacryloyl (GelMA) hydrogels are promising materials for tissue engineering applications due to their biocompatibility and tunable properties. However, the time-consuming process of preparing GelMA hydrogels with desirable properties for specific biomedical applications limits their clinical use. Visible-light-induced cross-linking is a well-known method for the preparation of GelMA hydrogels; however, a comprehensive investigation on the influence of critical parameters such as Eosin Y (EY), triethanolamine (TEA), and N-vinyl-2-pyrrolidone (NVP) concentrations on the stiffness and gelation time has yet to be performed. In this study, we systematically investigated the effect of these critical parameters on the stiffness and gelation time of GelMA hydrogels. We developed an artificial neural network (ANN) model with three input variables, EY, TEA, and NVP concentrations, and two output variables, Young's modulus and gelation time, derived from our experimental design. Through the alteration of individual chemical concentrations, [EY] between 0.005 and 0.5 mM and [TEA] and [NVP] between 10 and 1000 mM, we studied the impact of these alterations on the real-time values of stiffness and gelation time. Furthermore, we demonstrated the validity of the ANN model in predicting the properties of GelMA hydrogels. We also studied cell survival to establish nontoxic concentration ranges for each component, enabling safer use of GelMA hydrogels in relevant biomedical applications. Our results showed that the ANN model can accurately predict the properties of GelMA hydrogels, allowing for the synthesis of hydrogels with desirable stiffness for various biomedical applications. In conclusion, our study provides a comprehensive library that characterizes the stiffness and gelation time and demonstrates the potential of the ANN model to predict these properties of GelMA hydrogels depending on the critical parameters. The ANN models developed here can facilitate the optimization of GelMA hydrogels with the most efficient mechanical properties that resemble a native extracellular matrix and better address the need in the in vivo microenvironment. The approach of this study is to bring research about the synthesis of GelMA hydrogels to a new level where the synthesis of these hydrogels can be standardized with minimum cost and effort.

Proof of concept: Predicting distress in cancer patients using back propagation neural network (BPNN)

Background

Research findings suggest that a significant proportion of individuals diagnosed with cancer, ranging from 25% to 60%, experience distress and require access to psycho-oncological services. Until now, only contemporary approaches, such as logistic regression, have been used to determine predictors of distress in oncological patients. To improve individual prediction accuracy, novel approaches are required. We aimed to establish a prediction model for distress in cancer patients based on a back propagation neural network (BPNN).

Methods

Retrospective data was gathered from a cohort of 3063 oncological patients who received diagnoses and treatment spanning the years 2011-2019. The distress thermometer (DT) has been used as screening instrument. Potential predictors of distress were identified using logistic regression. Subsequently, a prediction model for distress was developed using BPNN.

Results

Logistic regression identified 13 significant independent variables as predictors of distress, including emotional, physical and practical problems. Through repetitive data simulation processes, it was determined that a 3-layer BPNN with 8 neurons in the hidden layer demonstrates the highest level of accuracy as a prediction model. This model exhibits a sensitivity of 79.0%, specificity of 71.8%, positive predictive value of 78.9%, negative predictive value of 71.9%, and an overall coincidence rate of 75.9%.

Conclusion

The final BPNN model serves as a compelling proof of concept for leveraging artificial intelligence in predicting distress and its associated risk factors in cancer patients. The final model exhibits a remarkable level of discrimination and feasibility, underscoring its potential for identifying patients vulnerable to distress.

Using water quality parameters to prediction of the ion-based trihalomethane by an artificial neural network model

Trihalomethanes (THMs) are the first disinfectant by-products in the drinking water distribution network and are classified as potential carcinogens. The presence of THMs in chlorinated water depends on the pH, water temperature, contact time between water and chlorine, type and dose of disinfection, bromide ion concentration, and type and concentration of natural organic materials (NOMs). In the present study, the formation of THMs was evaluated by six simple and easy water quality parameters and modeled by an artificial neural network (ANN) approach through five water distribution networks (WDNs) and the Karoun River in Khuzestan province. The results of this study that was conducted from October 2014 to September 2015 showed that THM concentration ranged in five WDNs, including Shoushtar, Ahvaz (2), Ahvaz (3), Mahshahr, Khorramshahr, and total WDNs through N.D.-9.39 µg/L, 7.12-28.60, 38.16-67.00, 17.15-90.46, 15.14-29.99, and N.D.-156, respectively. The concentration of THMs exceeded Iran and EPA standards in many cases in Mahshahr and Khorramshahr WDNs. Evaluation of R², MSE, and RMSE showed the appropriate correlation between measured and modeled THMs, indicating a reasonable ANN potential for estimating THM formation in water sources.

Latest insights on separation and storage of carbon compounds in buildings towards sustainable environment: Recent innovations, challenges, future perspectives and application of machine learning

Throughout the past few decades, scientific agencies have paid a lot of attention to environmental issues such as acid rain, water poisoning, and global warming. In order to solve these environmental problems, metal-organic frameworks (MOFs), which are made up of metal ions and/or clusters attached to organic ligands, have shown some promise. With a focus on the usage of MOFs, this paper examines the most recent developments, difficulties, and potential future directions in the separation and storage of carbon compounds in buildings for a sustainable environment. The importance of using MOFs in decarbonizing water systems and lowering environmental concerns in buildings is highlighted in the research. It addresses the most recent developments in the use of MOFs for renewable energy, such as the elimination of dangerous gases like CO2 and CH4 from water systems. The article also looks at how MOFs might be used to decarbonize water systems in structures, with a focus on how carbon-containing compounds are stored chemically and physically using artificial neural network models. MOFs are a potential solution for renewable energy and environmental remediation in buildings because they have special physical and chemical characteristics like adjustable pores, high porosity, and tiny pore size. The report offers insights into existing treatments and invites academics to investigate MOFs' potential for resolving environmental problems in order to create a sustainable environment in buildings.

Real-time monitoring of focused ultrasound therapy using intelligence-based thermography: A feasibility study

Focused ultrasound (FUS) therapy has been widely studied for breast cancer treatment due to its potential as a fully non-invasive method to improve cosmetic and oncologic results. However, real-time imaging and monitoring of the therapeutic ultrasound delivered to the target area remain challenges for precision breast cancer therapy. The main objective of this study is to propose and evaluate a novel intelligence-based thermography (IT) method that can monitor and control FUS treatment using thermal imaging with the fusion of artificial intelligence (AI) and advanced heat transfer modeling. In the proposed method, a thermal camera is integrated into FUS system for thermal imaging of the breast surface, and an AI model is employed for the inverse analysis of the surface thermal monitoring, thereby estimating the features of the focal region. This paper presents experimental and computational studies conducted to assess the feasibility and efficiency of IT-guided FUS (ITgFUS). Tissue phantoms, designed to mimic the properties of breast tissue, were used in the experiments to investigate detectability and the impact of temperature rise at the focal region on the tissue surface. Additionally, an AI computational analysis employing an artificial neural network (ANN) and FUS simulation was carried out to provide a quantitative estimation of the temperature rise at the focal region. This estimation was based on the observed temperature profile on the breast model's surface. The results proved that the effects of temperature rise at the focused area could be detected by the thermal images acquired with thermography. Moreover, it was demonstrated that the AI analysis of the surface temperature measurement could result in near real-time monitoring of FUS by quantitative estimation of the temporal and spatial temperature rise profiles at the focal region.

Source-apportionment and spatial distribution analysis of VOCs and their role in ozone formation using machine learning in central-west Taiwan

This study assessed the machine learning based sensitivity analysis coupled with source-apportionment of volatile organic carbons (VOCs) to look into new insights of O₃ pollution in Yunlin County located in central-west region of Taiwan. One-year (Jan 1 to Dec 31, 2021) hourly mass concentrations data of 54 VOCs, NO_X, and O₃ from 10 photochemical assessment monitoring stations (PAMs) in and around the Yunlin County were analyzed. The novelty of the study lies in the utilization of artificial neural network (ANN) to evaluate the contribution of VOCs sources in O₃ pollution in the region. Firstly, the station specific source-apportionment of VOCs were carried out using positive matrix factorization (PMF)-resolving six sources viz. AAM: aged air mass, CM: chemical manufacturing, IC: Industrial combustion, PP: petrochemical plants, SU: solvent use and VE: vehicular emissions. AAM, SU, and VE constituted cumulatively more than 65% of the total emission of VOCs across all 10 PAMs. Diurnal and spatial variability of source-segregated VOCs showed large variations across 10 PAMs, suggesting for distinctly different impact of contributing sources, photo-chemical reactivity, and/or dispersion due to land-sea breezes at the monitoring stations. Secondly, to understand the contribution of controllable factors governing the O₃ pollution, the output of VOCs source-contributions from PMF model along with mass concentrations of NO_X were standardized and first time used as input variables to ANN, a supervised machine learning algorithm. ANN analysis revealed following order of sensitivity in factors governing the O₃ pollution: VOCs from IC > AAM > VE ≈ CM ≈ SU > PP ≈ NO_X. The results indicated that VOCs associated with IC (VOCs-IC) being the most sensitive factor which need to be regulated more efficiently to quickly mitigate the O₃ pollution across the Yunlin County.

A comparative study of data-driven models for runoff, sediment, and nitrate forecasting

Effective prediction of qualitative and quantitative indicators for runoff is quite essential in water resources planning and management. However, although several data-driven and model-driven forecasting approaches have been employed in the literature for streamflow forecasting, to our knowledge, the literature lacks a comprehensive comparison of well-known data-driven and model-driven forecasting techniques for runoff evaluation in terms of quality and quantity. This study filled this knowledge gap by comparing the accuracy of runoff, sediment, and nitrate forecasting using four robust data-driven techniques: artificial neural network (ANN), long short-term memory (LSTM), wavelet artificial neural network (WANN), and wavelet long short-term memory (WLSTM) models. These comparisons were performed in two main tiers: (1) Comparing the machine learning algorithms' results with the model-driven approach; In order to simulate the runoff, sediment, and nitrate loads, the Soil and Water Assessment Tool (SWAT) model was employed, and (2) Comparing the machine learning algorithms with each other; The wavelet function was utilized in the ANN and LSTM algorithms. These comparisons were assessed based on the substantial statistical indices of coefficient of determination (R-Squared), Nash-Sutcliff efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). Finally, to prove the applicability and efficiency of the proposed novel framework, it was successfully applied to Eagle Creek Watershed (ECW), Indiana, U.S. Results demonstrated that the data-driven algorithms significantly outperformed the model-driven models for both the calibration/training and validation/testing phases. Furthermore, it was found that the coupled ANN and LSTM models with wavelet function led to more accurate results than those without this function.

Energy-Efficient Hardware Implementation of Fully Connected Artificial Neural Networks Using Approximate Arithmetic Blocks

In this paper, we explore efficient hardware implementation of feedforward artificial neural networks (ANNs) using approximate adders and multipliers. Due to a large area requirement in a parallel architecture, the ANNs are implemented under the time-multiplexed architecture where computing resources are re-used in the multiply accumulate (MAC) blocks. The efficient hardware implementation of ANNs is realized by replacing the exact adders and multipliers in the MAC blocks by the approximate ones taking into account the hardware accuracy. Additionally, an algorithm to determine the approximate level of multipliers and adders due to the expected accuracy is proposed. As an application, the MNIST and SVHN databases are considered. To examine the efficiency of the proposed method, various architectures and structures of ANNs are realized. Experimental results show that the ANNs designed using the proposed approximate multiplier have a smaller area and consume less energy than those designed using previously proposed prominent approximate multipliers. It is also observed that the use of both approximate adders and multipliers yields, respectively, up to 50% and 10% reduction in energy consumption and area of the ANN design with a small deviation or better hardware accuracy when compared to the exact adders and multipliers.

Optimization of identifying insulinaemic pharmacokinetic parameters using artificial neural network

BACKGROUND AND OBJECTIVE

The identification of insulinaemic pharmacokinetic parameters using the least-squares criterion approach is easily influenced by outlying data due to its sensitivity. Furthermore, the least-squares criterion has a tendency to overfit and produce incorrect results. Hence, this research proposes an alternative approach using the artificial neural network (ANN) with two hidden layers to optimize the identifying of insulinaemic pharmacokinetic parameters. The ANN is selected for its ability to avoid overfitting parameters and its faster speed in processing data.

METHODS

18 voluntarily participants were recruited from the Canterbury and Otago region of New Zealand to take part in a Dynamic Insulin Sensitivity and Secretion Test (DISST) clinical trial. A total of 46 DISST data were collected. However, due to ambiguous and inconsistency, 4 data had to be removed. Analysis was done using MATLAB 2020a.

RESULTS AND DISCUSSION

Results show that, with 42 gathered dataset, the ANN generates higher gains, ∅_P = 20.73 [12.21, 28.57] mU·L·mmol^-1·min^-1 and ∅_D = 60.42 [26.85, 131.38] mU·L·mmol^-1 as compared to the linear least square method, ∅_P = 19.67 [11.81, 28.02] mU·L·mmol^-1 ·min^-1 and ∅_D = 46.21 [7.25, 116.71] mU·L·mmol^-1. The average value of the insulin sensitivity (SI) of ANN is lower with, SI = 16 × 10^-4 L·mU^-1 ·min^-1 than the linear least square, SI = 17 × 10^-4 L·mU^-1 ·min^-1.

CONCLUSION

Although the ANN analysis provided a lower SI value, the results were more dependable than the linear least square model because the ANN approach yielded a better model fitting accuracy than the linear least square method with a lower residual error of less than 5%. With the implementation of this ANN architecture, it shows that ANN able to produce minimal error during optimization process particularly when dealing with outlying data. The findings may provide extra information to clinicians, allowing them to gain a better knowledge of the heterogenous aetiology of diabetes and therapeutic intervention options.

Risk scoring based on DNA methylation-driven related DEGs for colorectal cancer prognosis with systematic insights

Colorectal cancer is a common malignant tumor of the digestive tract. Despite advances in diagnostic techniques and medications. Its prognosis remains challenging. DNA methylation-driven related circulating tumor cells have attracted enormous interest in diagnosing owing to their non-invasive nature and early recognition properties. However, the mechanism through which risk biomarkers act remains elusive. Here, we designed a risk model based on differentially expressed genes, DNA methylation, robust, and survival-related factors in the framework of Cox regression. The model has satisfactory performance and is independently verified by an external and isolated dataset in terms of C-index value, ROC, and tROC. The model was applied to Colorectal cancer patients who were subsequently divided into high- and low-risk groups. Functional annotations, genomic alterations, tumor immune environment, and drug sensitivity were analyzed. We observed that up-regulated genes are associated with epithelial cell differentiation and MAPK signaling pathways. The down-regulated genes are related to IL-7 signaling and apoptosis-induced DNA fragmentation. Interestingly, the immune system was inhibited in high-risk groups. High-frequency mutation genes tend to co-occur. High-risk score patients are related to copy number amplification events. To address the challenges, we suggested eleven and twenty-one drugs that are sensitive to low- and high-risk patients. Finally, an artificial neural network was provided to evaluate the immunotherapeutic efficiency. Taken together, the findings demonstrated that our risk score model is robust and reliable for evaluating the prognosis with novel diagnostic and treatment targets. It also yields benefits for the treatment and provides unique insights into developing therapeutic strategies.

Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high ("H") and low ("L") level of saccharification a chosen carrier (potato maltodextrin).

Investigation of enhanced H2 production from municipal solid waste gasification via artificial neural network with data on tar compounds

An artificial neural network (ANN) is a biologically inspired computational technique that imitates the behavior and learning process of the human brain. In this study, ANN technique was applied to assess the gasification of municipal solid waste (MSW) with the aim of enhancing the H₂ production. The experiments were conducted using a horizontal tube reactor under different parameters: temperatures, MSW loadings, residence times, and equivalence ratios. The input and output variables (released gases) were tested and trained using back-propagation algorithm, and the data distribution by K-fold contrivance. The values of the training (80% data) and validation (20% data) dataset were found satisfactory. The values of regression coefficient (R²) for the training phase were lied between 0.9392 and 0.9991, and 0.9363 and 0.993824 for the testing phase. Whereas; the values of root mean square error (RSME) for the training phase were lied between 0.4111 and 0.8422, and between 0.1476 and 0.7320 for the testing phase. Higher H₂ production of 42.1 vol% was produced at the higher reaction temperature of 900 °C with LHV of 11.2 MJ/Nm³. According to the tar analysis, the dominant compounds were aromatics (17 compounds) followed by polycyclic aromatic, phenyl, aliphatic, aromatic heterocyclic, polycyclic, and aromatic ketone compounds.

Machine learning and statistical analysis for biomass torrefaction: A review

Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kinetic analysis, process modeling, and computational fluid dynamics, have been used to explain torrefaction performance and characteristics. However, they may be insufficient in actual applications because of providing only some specific solutions. Machine learning (ML) and statistical approaches are powerful tools for analyzing and predicting torrefaction outcomes and even optimizing the thermal process for its utilization. This state-of-the-art review aims to present ML-assisted torrefaction. Artificial neural networks, multivariate adaptive regression splines, decision tree, support vector machine, and other methods in the literature are discussed. Statistical approaches (SAs) for torrefaction, including Taguchi, response surface methodology, and analysis of variance, are also reviewed. Overall, this review has provided valuable insights into torrefaction optimization, which is conducive to biomass upgrading for achieving net zero.

A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images

PURPOSE

Screening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalcification on the mammographic image are two important indicators. The objective of this study was to evaluate the feasibility of the automatic separation of images of breast tissue microcalcifications and also to evaluate its accuracy.

METHODS

The research was carried out by using two techniques of image enhancement and highlighting of breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method. After determining the clusters of breast tissue microcalcifications, the clusters are classified using the decision tree classification algorithm. Then, for segmentation, samples suspected of microcalcification are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of an artificial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous work.

RESULTS

The results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%.

CONCLUSION

The result indicates that the proposed approach can be applied to ensure breast cancer diagnosis.

An approach to removing COD and BOD based on polycarbonate mixed matrix membranes that contain hydrous manganese oxide and silver nanoparticles: A novel application of artificial neural network based simulation in MATLAB

This study aimed to determine the efficacy of novel ultrafiltration and mixed matrix membrane (MMM) composed of hydrous manganese oxide (HMO) and silver nanoparticles (Ag-NPs) for the removal of biological oxygen demand (BOD) and chemical oxygen demand (COD). In the polycarbonate (PC) MMM, the weight percent of HMO and Ag-NP has been increased from 5% to 10%. A neural network (ANN) was used in this study to compare PC-HMO and Ag-NP. MMM was evaluated in combination with HMO and Ag-NP loadings in order to assess their effects on pure water flux, mean pore size, porosity, and efficacy in removing BOD and COD. HMO and Ag-NPs can decrease membrane porosity in the casting solution while increasing mean pore size. According to the study's findings, the artificial neural network model appears to be highly appropriate for predicting the removal of BOD and COD. To develop a successful model, a suitable input dataset was selected, which consisted of BOD and COD. An ideal model architecture for MMM was proposed based on an optimal number of hidden layers (2 layers) and neurons (5-8 neurons). Experiments and predicted data show a strong correlation between the developed models. BOD was predicted with an excellent R2 and a low root mean square error (RMSE) of 0.99 and 0.05%, respectively, while COD was predicted with an excellent R2 and a low RMSE of 0.99 and 0.09%, respectively. Based on the results, Ag-NP was found to be an excellent candidate for the preparation of MMMs as well as convenient for the removal of BOD and COD from polluted water sources.

The development and validation of a prediction model of lithium carbonate blood concentration by artificial neural network: a retrospective study

BACKGROUND

Bipolar disorder (BD) is common in clinical practice. Lithium (Li) carbonate is often used in the treatment of BD. However, the therapeutic dose of Li carbonate is close to the toxic dose, and Li poisoning is prone to occur. Precise prediction of Li concentration will help clinician to identify patients at high risk of toxic dose of Li carbonate. The purpose of this study was to establish a model for predicting the blood concentration of Li carbonate through an artificial neural network (ANN), and to provide a basis for the clinical rapid and effective formulation of individualized dosing regimens.

METHODS

Patients with BD who were diagnosed and treated in our hospital from October 2016 to April 2021 were enrolled as the research participants. We collected patient demographic data, including age and gender; physical examination information, including height and weight; laboratory test results, including liver and renal function, and Li concentrations; medication information, including Li carbonate usage, concomitant medications, and dose; and information on comorbidities and adverse reactions. The Li concentration data of 236 patients were randomly divided into 2 groups: 195 cases in the training group and 41 cases in the test group. The ANN fitting module of SPSS 26.0 was used for modeling and prediction.

RESULTS

A total of 236 patients with BD were included in this study. Daily dose (before testing Li concentration), age, alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB), total bilirubin (TBIL), and creatinine (Cr), and co-administered zopiclone, quinine, tipine, lorazepam, olanzapine, valproate, metoprolol, and statins were used as model input variables for training. The test results of the model in the testing group showed that the correlation coefficient between the predicted value of Li concentration and the actual value was r=0.9883, r2=0.9767, P<0.001, the prediction error range was -0.05 to 0.07 mmol/L, and the deviation range was -18.52 to 13.04%; the mean absolute error was 0.03, and the mean prediction error deviation was between -10% and 10% in 33 cases (80.5%).

CONCLUSIONS

The correlation, accuracy, and precision of ANN prediction are worthy to be further investigated to predict the blood concentration of Li carbonate.

Identifying contributing factors to China's declining share of renewable energy consumption: no silver bullet to decarbonisation

Renewable energy consumption (REC) holds the key to sustainable development. Therefore, many studies have considered the role of REC. However, the factors influencing the REC share in total energy usage (SREC) are not well investigated. Especially, the factors of China's fast-shrinking SREC are understudied. This research void on the world's largest renewable energy producer and consumer, i.e., China's decreasing SREC, is alarming and requires thorough investigation. Our study intends to fill this gap by analyzing the factors of China's decreasing SREC. The study uses both the conventional (descriptive and directional correlational analyses) and some unconventional (automatic linear modeling (ALM) and Artificial neural network (ANN) multilayer perceptron (MLP)) approach to investigate the factors of China's decreasing SREC. The initial hypothesis testing and most reliable model validation were achieved via directional correlational (Pearson and Spearman) and ALM analyses. The ANN MLP (two hidden layers) indicated that the most critical factor is "Combustible renewables and waste," with a 100% normalized importance. It was followed by "urbanization (64.2%), gross savings (56.1%), and alternative and nuclear energy (38%)," respectively. It is suggested that the Chinese government and private investors prioritize their investments based on factors' importance ranking.

Experimental analysis and parameter optimization on the reduction of NOx from diesel engine using RSM and ANN Model

The major emission sources of NO_X are from automobiles, trucks, and various non-road vehicles, power plants, coal fired boilers, cement kilns, turbines, etc. Plasma reactor technology is widely used in gas conversion applications, such as NOx conversion into useful chemical by-product. Among the plasma treatment techniques, nonthermal plasma (NTP) is widely used because it does not cause any damage to the surfaces of the reacting chamber. In this proposed work, the feasibility of Dielectric Barrier Discharge (DBD) reactor-based nonthermal plasma (NTP) process is examined based on four operating parameters including NOx concentration (300-400 ppm), gas flow rate (2-6 lpm), applied plasma voltage (20-30 kVpp), and electrode gap (3-5 mm) for removing NOx gas from diesel engine exhaust. Optimization of NTP process parameters has been carried out using response surface-based Box-Behnken design (BBD) method and artificial neural network (ANN) method and compared with the performance measures such as R², MSE (mean square error), RMSE (root mean square error), and MAPE (mean absolute percentage error). Two kinds of analysis were carried out based on (1) NOx removal efficiency and (2) energy efficiency. Based on the simulation studies carried out for Nox removal efficiency, the RSM methodology produces the performance measures, 0.98 for R², 1.274 for MSE, 1.128 for RMSE, and 2.053 for MAPE, and for ANN analysis method, 0.99 for R², 2.167 for MSE, 1.472 for RMSE, and 1.276 for MAPE. These results shows that ANN method is having enhanced performance measures. For the second case, based on the energy efficiency study, the R², MSE, RMSE, and MAPE values from the RSM model are 0.97, 2.230, 1.493, and 2.903 respectively. Similarly based on ANN model, the R², MSE, RMSE, and MAPE values are 0.99, 0.246, 0.46, and 0.615, respectively. From the performance measures, it is found that the ANN model is accurate than the RSM model in predicting the NOx removal/reduction and efficiency. These models demonstrate that they have strong agreement with the experimental results. The experimental results are indicated that optimum conditions arrived based on the RSM model resulted in a maximum NOx reduction of 60.5% and an energy efficiency of 66.24 g/J. The comparison between the two models confirmed the findings, whereas this ANN model displayed a stronger correlation to the experimental evidence.

A green energy research: forecasting of wind power for a cleaner environment using robust hybrid metaheuristic model

Wind is a stochastic and intermittent renewable energy source. Due to its nature, it is extremely hard to forecast wind power. Accurate wind power forecasting can be encouraging and motivating for investors to shed light on future uncertainties caused by global warming. Thus, CO₂ and other greenhouse gases (GHG) which are harmful to the environment will not be released into the atmosphere, while generating electrical energy. This paper presents a novel precise, fast and powerful hybrid metaheuristic wind power forecasting approach based on statistical and mathematical data from real weather stations. The model was developed as a hybrid metaheuristic algorithm based on artificial neural networks (ANNs), particle swarm optimization (PSO) and radial movement optimization (RMO). Real-time wind data was gathered from wind measuring stations (WMS) at two separate places in Burdur and Osmaniye cities, Turkey. The key contribution of this new model is the ability to perform wind power forecasting studies, without needing wind speed data, with high accuracy and rapid solutions. Also, wind power forecasting studies with high accuracy have been carried out despite the height differences between the sensors. That is, for WMS-1 and WMS-2, it has succeeded the wind power forecasting at 61 m and 60.3 m using temperature (3 m), humidity (3 m) and pressure (3.5 m) data. The performance results were presented in tables and graphs.

Capability assessment of conventional and data-driven models for prediction of suspended sediment load

Information about suspended sediment concentration (SSC) in the stream is vital for sustainability of water conservation and erosion control planning, designing and monitoring. In this research, prediction of SSC has been done using artificial neural network (ANN), support vector regression (SVR) and multi-linear regression (MLR) models. Performance evaluation of developed models has been carried out on the basis of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (CE) and pooled average relative error (PARE). Cross-correlation function (CCF) validated that gamma test (GT) is an appropriate tool for the selection of most responsive input variables. On the basis of GT and CCF, GT-6 model was selected as the model with most effective input variables, with the values of gamma, standard error and V-ratio as 0.0643, 0.00583 and 0.2570, respectively. The ANN (6-3-1) model performed better than the other single and double hidden layered ANN models with the values of r, RMSE, CE and PARE as 0.939, 0.0063 g/l, 85.17 and 0.0160, respectively. The performance of the SVR model was found better with the values of r, RMSE, CE and PARE as 0.906, 0.018 g/l, 79.09 and 0001, respectively, but slightly poor than the selected ANN (6-3-1) model. The values of r, RMSE, CE and PARE were found as 0.899, 0.0312 g/l, 65.15 and - 0.0031, respectively, in the case of MLR model. The present study revealed that among the ANN, SVR and MLR models, the ANN model with a single hidden layer is most suitable for observed SSC. The present study offers the simple efficient model to estimate the suspended sediment concentration in the stream with minimum error using limited data set.

An investigation and comparison of machine learning approaches for intrusion detection in IoMT network

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems. But it comes with its drawbacks essentially with patient's information privacy and security related issues that comes from IoMT architecture. Using obsolete systems can bring security vulnerabilities and draw attacker's attention emphasizing the need for effective solution to secure and protect the data traffic in IoMT network. Recently, intrusion detection system (IDS) is regarded as an essential security solution for protecting IoMT network. In the past decades, machines learning (ML) algorithms have demonstrated breakthrough results in the field of intrusion detection. Notwithstanding, to our knowledge, there is no work that investigates the power of machines learning algorithms for intrusion detection in IoMT network. This paper aims to fill this gap of knowledge investigating the application of different ML algorithms for intrusion detection in IoMT network. The investigation analysis includes ML algorithms such as K-nearest neighbor, Naïve Bayes, support vector machine, artificial neural network and decision tree. The benchmark dataset, Bot-IoT which is publicly available with comprehensive set of attacks was used to train and test the effectiveness of all ML models considered for investigation. Also, we used comprehensive set of evaluation metrics to compare the power of ML algorithms with regard to their detection accuracy for intrusion in IoMT networks. The outcome of the analysis provides a promising path to identify the best the machine learning approach can be used for building effective IDS that can safeguard IoMT network against malicious activities.

Prediction of crude oil prices in COVID-19 outbreak using real data

The world has been undergoing a global economic recession for almost two years because of the health crisis stemming from the outbreak and its effects have still continued so far. Especially, COVID-19 reduced consumer spending due to social isolation, lockdown and travel restrictions in 2020. As a result of this, with social and economic life coming to a standstill, oil prices plummeted. With the ongoing uncertainty concerning the COVID-19 pandemic, it has been of great importance for all economic agents to predict crude oil prices. The objective of this paper is to improve a model in order to make more accurate predictions for crude oil price movements. The performance of this model is assessed in terms of some significant criteria comparing our model with its counterparts as well as artificial neural networks (ANNs) and support vector machine (SVM) methods. As for these criteria, root mean square error (RMSE) and mean absolute error (MAE) results show that this model outperforms other models in forecasting crude oil prices. Further, the simulation results for 2021 show that the daily crude oil price forecasts are almost close to the real oil prices. Oil price forecasting has become more and more important for economic agents in COVID-19 period. A consistent model is required to cope with the movements in crude oil prices. A novel method combining fuzzy time series and the greatest integer function is developed. The results show that our model outperforms other counterparts or ANN and SVM methods. We capture non-linearity and volatility in crude oil prices.

Time series forecasting for uni- variant data using hybrid GA-OLSTM model and performance evaluations

Time series forecasting of uni-variant rainfall data is done using a hybrid genetic algorithm integrated with optimized long-short term memory (GA-OLSTM) model. The parameters included for the valuation of the efficiency of the considered model, were mean square error (MSE), root mean square error (RMSE), cosine similarity (CS) and correlation coefficient (r). With various epochs like 5, 10, 15 and 20, the optimal window size and the number of units were observed using the GA search algorithm which was found to be (49, 9), (12, 8), (40, 8), and (36, 2) respectively. The computed MSE, RMSE, CS and r for 10 epochs were found to be 0.006, 0.078, 0.910 and 0.858 respectively for the LSTM model, whereas the same parameters were computed using the Hybrid GA-OLSTM model was 0.004, 0.063, 0.947 and 0.917 respectively. The experimental results expressed that the Hybrid GA-OLSTM model gave significantly better results comparing the LSTM model for 10 epochs has been discussed in this research article.

Modeling and multi-objective optimization of commercial ethylene oxide reactor to strike a delicate balance between profit and negative environmental impact

The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to maintain a delicate balance between profit and environmental impact. Here, ethylene oxide (EO) production plant has been taken as a case study. The production of EO takes place in a multiphase catalytic reactor, the reliable first principle-based model of which is still not available in the literature. Artificial neural network (ANN) was therefore applied to develop a data-driven model of the complex reactor with the help of actual industrial data. The model successfully built up a correlation between the catalyst selectivity and other operational parameters. This model was used to establish two objective functions, profit and environmental impact. In this paper, the negative environmental impact has been designated by Eco-indicator 99, which considers all the negative health and environmental impacts of a certain product. A recently developed metaheuristic optimization technique, namely multi-objective firefly (MOF) algorithm, was used to develop Pareto diagram of profit vs. Eco-99. The Pareto diagram will help the plant engineers to make strategy on what operating conditions to be maintained to make a delicate balance between profit and environmental impact. It was also found that by applying this modeling and optimization technique, for a 130 kTA EO plant, approximately 7048 t/year of carbon dioxide can be saved from emission into the atmosphere.

An artificial neural network chip based on two-dimensional semiconductor

Recently, research on two-dimensional (2D) semiconductors has begun to translate from the fundamental investigation into rudimentary functional circuits. In this work, we unveil the first functional MoS₂ artificial neural network (ANN) chip, including multiply-and-accumulate (MAC), memory and activation function circuits. Such MoS₂ ANN chip is realized through fabricating 818 field-effect transistors (FETs) on a wafer-scale and high-homogeneity MoS₂ film, with a gate-last process to realize top gate structured FETs. A 62-level simulation program with integrated circuit emphasis (SPICE) model is utilized to design and optimize our analog ANN circuits. To demonstrate a practical application, a tactile digit sensing recognition was demonstrated based on our ANN circuits. After training, the digit recognition rate exceeds 97%. Our work not only demonstrates the protentional of 2D semiconductors in wafer-scale integrated circuits, but also paves the way for its future application in AI computation.

Artificial neural network (ANN) modeling for the prediction of odor emission rates from landfill working surface

Landfills release significant odorous compounds from the working surface, and their emission rates are crucial for odor and health risk assessment. A total of 99 valid datasets of odor emissions from a landfill working surface were obtained from in situ monitoring for 9 months. Meteorological parameters (temperature, humidity, atmospheric pressure) and waste properties (contents of protein, lipid, carbohydrate, ash, and moisture) were used to construct artificial neural network (ANN) models for the emission rate prediction of typical compounds. The optimal structures and performance of the ANN models were determined by comparing and training with different structural configurations. The ANN models with genetic algorithm (GA) optimization show better performance than those without GA. With the data distribution of input parameters, the ranges of the emission rates of typical compounds were predicted by combining the established ANN models and the Monte Carlo approach. The sensitivity and uncertainty analyses revealed that temperature, atmospheric pressure, protein and lipid contents are parameters sensitive to emission rates, and meteorological parameters have significant impacts on the uncertainty. The established ANN models for the prediction of emission rates can provide scientific evidence and an approach to assess and control the odor and health risk in waste sectors.

Integrating Taguchi method and artificial neural network for predicting and maximizing biofuel production via torrefaction and pyrolysis

Artificial neural network (ANN) is one kind of artificial intelligence in the computing system that aims to process information as the way neurons in the human brain. In this study, the combination of the Taguchi method and ANN are used to maximize and predict biofuel yield from spent mushroom substrate torrefaction and pyrolysis via microwave irradiation. The Taguchi method is utilized to design the multiple factors (particle size, catalyst, power, and magnetic agent) and levels of experiment parameters. The highest total biofuel yield (biochar + bio-oil) is 99.42%, accomplished by a combination of 355 µm particle size, 300 mg·g-SMS^-1 catalyst, 900 W power, and 300 mg·g-SMS^-1 magnetic agent. ANN with one hidden layer shows the outstanding linear regression predictions for the highest biofuel yields (biochar 0.9999 and bio-oil 0.9998). This high linear regression indicates that ANN with a quick propagation algorithm is an appropriate approach for predicting biofuel conversion.

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

With the continuous deepening of Artificial Neural Network (ANN) research, ANN model structure and function are improving towards diversification and intelligence. However, the model is more evaluated from the pros and cons of the problem-solving results and the lack of evaluation from the biomimetic aspect of imitating neural networks is not inclusive enough. Hence, a new ANN models evaluation strategy is proposed from the perspective of bionics in response to this problem in the paper. Firstly, four classical neural network models are illustrated: Back Propagation (BP) network, Deep Belief Network (DBN), LeNet5 network, and olfactory bionic model (KIII model), and the neuron transmission mode and equation, network structure, and weight updating principle of the models are analyzed qualitatively. The analysis results show that the KIII model comes closer to the actual biological nervous system compared with other models, and the LeNet5 network simulates the nervous system in depth. Secondly, evaluation indexes of ANN are constructed from the perspective of bionics in this paper: small-world, synchronous, and chaotic characteristics. Finally, the network model is quantitatively analyzed by evaluation indexes from the perspective of bionics. The experimental results show that the DBN network, LeNet5 network, and BP network have synchronous characteristics. And the DBN network and LeNet5 network have certain chaotic characteristics, but there is still a certain distance between the three classical neural networks and actual biological neural networks. The KIII model has certain small-world characteristics in structure, and its network also exhibits synchronization characteristics and chaotic characteristics. Compared with the DBN network, LeNet5 network, and the BP network, the KIII model is closer to the real biological neural network.

Physicochemical parameters combined flash GC e-nose and artificial neural network for quality and volatile characterization of vinegar with different brewing techniques

Vinegar is a kind of traditional fermented food, there are significant variances in quality and flavor due to differences in raw ingredients and processes. The quality assessment and flavor characteristics of 69 vinegar samples with 5 brewing processes were analyzed by physicochemical parameters combined with flash gas chromatography (GC) e-nose. The evaluation system of quality and the detection method of flavor profile were established. 17 volatile flavor compounds and potential flavor differential compounds of each brewing process were identified. The artificial neural network (ANN) analysis model was established based on the physicochemical parameters and the analysis of flash GC e-nose. Although the physicochemical parameters were more intuitive in quality evaluating, the flash GC e-nose could better reflect the flavor characteristics of vinegar samples and had better fitting, prediction and discrimination ability, the correct rates of training and prediction of flash GC e-nose trained ANN model were 98.6% and 96.7%, respectively.

Grade diagnosis of human glioma using Fourier transform infrared microscopy and artificial neural network

The World Health Organization (WHO) grade diagnosis of cancer is essential for surgical outcomes and patient treatment. Traditional pathological grading diagnosis depends on dyes or other histological approaches, and the result interpretation highly relies on the pathologists, making the process time-consuming (>60 min, including the steps of dewaxing to water and H&E staining), resource-wasting, and labor-intensive. In the present study, we report an alternative workflow that combines the Fourier transform infrared (FTIR) microscopy and artificial neural network (ANN) to diagnose the grade of human glioma in a way that is faster (~20 min, including the processes of sample dewaxing, spectra acquisition and analysis), accurate (the prediction accuracy, specificity and sensitivity can reach above 99%), and without reagent. Moreover, this method is much superior to the common classification method of principal component analysis-linear discriminate analysis (PCA-LDA) (the prediction accuracy, specificity and sensitivity are only 87%, 89% and 86%, respectively). The ANN mainly learned the characteristic region of 800-1800 cm^-1 to classify the major histopathologic classes of human glioma. These results demonstrate that the grade diagnosis of human glioma by FTIR microscopy plus ANN can be streamlined, and could serve as a complementary pathway that is independent of the traditional pathology laboratory.

Enhanced precision of real-time control photothermal therapy using cost-effective infrared sensor array and artificial neural network

Photothermal therapy (PTT) requires tight thermal dose control to achieve tumor ablation with minimal thermal injury on surrounding healthy tissues. In this study, we proposed a real-time closed-loop system for monitoring and controlling the temperature of PTT using a non-contact infrared thermal sensor array and an artificial neural network (ANN) to induce a predetermined area of thermal damage on the tissue. A cost-effective infrared thermal sensor array was used to monitor the temperature development for feedback control during the treatment. The measured and predicted temperatures were used as inputs of fuzzy control logic controllers that were implemented on an embedded platform (Jetson Nano) for real-time thermal control. Three treatment groups (continuous wave = CW, conventional fuzzy logic = C-Fuzzy, and ANN-based predictive fuzzy logic = P-Fuzzy) were examined and compared to investigate the laser heating performance and collect temperature data for ANN model training. The ex vivo experiments validated the efficiency of fuzzy control with temperature method on maintaining the constant interstitial tissue temperature (80 ± 1.4 ^°C) at a targeted surface of the tissue. The linear relationship between coagulation areas and the treatment time was indicated in this study, with the averaged coagulation rate of 0.0196 cm²/s. A thermal damage area of 1.32 cm² (diameter ∼1.3 cm) was observed under P-Fuzzy condition for 200 s, which covered the predetermined thermal damage area (diameter ∼1 cm). The integration of real-time feedback temperature control with predictive ANN could be a feasible approach to precisely induce the preset extent of thermal coagulation for treating papillary thyroid microcarcinoma.

Modelling of adsorption of anionic azo dye using Strychnos potatorum Linn seeds (SPS) from aqueous solution with artificial neural network (ANN)

Synthetic dyes used in the textile and paper industries pose a major threat to the environment. In the present research work, the adsorption efficiency of the natural adsorbent Strychnos potatorum Linn (Fam: Loganiaceae) seeds were examined against the reactive orange-M2R dye from aqueous solution by varying the process conditions such as contact time, pH, adsorbent dosage, and initial dye concentration on adsorption of anionic azo dye. This study compares different types of artificial neural networks which are feedforward artificial neural network (FANN) and nonlinear autoregressive exogenous (NARX) model to predict the efficiency of a cost-effective natural adsorbent Strychnos potatorum Linn seeds on removing reactive orange-M2R dye from aqueous solution. Twelve training algorithms of neural network were compared, and the prediction on the adsorption performance of anionic azo dye from aqueous solution using Strychnos potatonum Linn seeds was evaluated by using the root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and accuracy. For FANN model, Levenberg-Marquardt (LM) backpropagation with 19 hidden neurons was selected as the optimum FANN model, with R² of 0.994 and accuracy of 87.20%, 98.21%, and 66.60% for training, testing, and validation datasets, respectively. For NARX model, LM with 8 hidden neurons was selected as the most suitable training algorithm, with R² value of more than 0.99 and accuracy of 88.00%, 90.91%, and 75.00% for training, testing, and validation datasets, respectively. NARX model accurately predicted the adsorption of anionic azo dye from aqueous solution using Strychnos potatonum Linn seeds with better performance than FANN model.

What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research

Artificial intelligence (AI) and machine learning (ML) are fascinating interdisciplinary scientific domains where machines are provided with an approximation of human intelligence. The conjecture is that machines are able to learn from existing examples, and employ this accumulated knowledge to fulfil challenging tasks such as regression analysis, pattern classification, and prediction. The horse biomechanical models have been identified as an alternative tool to investigate the effects of mechanical loading and induced deformations on the tissues and structures in humans. Many reported investigations into bone fatigue, subchondral bone damage in the joints of both humans and animals, and identification of vital parameters responsible for retaining integrity of anatomical regions during normal activities in all species are heavily reliant on equine biomechanical research. Horse racing is a lucrative industry and injury prevention in expensive thoroughbreds has encouraged the implementation of various measurement techniques, which results in massive data generation. ML substantially accelerates analysis and interpretation of data and provides considerable advantages over traditional statistical tools historically adopted in biomechanical research. This paper provides the reader with: a brief introduction to AI, taxonomy and several types of ML algorithms, working principle of a feedforward artificial neural network (ANN), and, a detailed review of the applications of AI, ML, and ANN in equine biomechanical research (i.e. locomotory system function, gait analysis, joint and bone mechanics, and hoof function). Reviewing literature on the use of these data-driven tools is essential since their wider application has the potential to: improve clinical assessments enabling real-time simulations, avoid and/or minimize injuries, and encourage early detection of such injuries in the first place.

Multi-objective optimization for optimal extraction of groundwater from a nitrate-contaminated aquifer considering economic-environmental issues: A case study

This paper focuses on the multi-objective optimization of the groundwater extraction scheme in the Bouein-Myandasht aquifer (Iran) in order to reduce the concentration of nitrate, originating from agricultural activities and wastewater absorbent wells. A simulation-optimization model coupling an artificial neural network (ANN) as the simulator with the non-dominated sorting genetic algorithm-type II (NSGA-II) as the optimizer, are employed. The simulator is trained by help of data generated by process-based simulation models for groundwater flow (MODFLOW) and solute transport (MT3D). The optimization objectives include (1) minimizing the contamination concentration and (2) maximizing the net benefit of the agricultural activities. The outcome of the simulation-optimization model is an optimized management strategy formed by the optimal values of the optimization parameters searched and obtained consisting of (1) seasonal groundwater extraction volume; (2) the ratio of the wastewater which should be treated before being leached into the groundwater through the absorbent wells; (3) the ratio of the fertilizers consumption; and (4) the cultivated area for each of the main crops in the study area. The results of the model suggest a groundwater extraction policy fulfilling the objectives of the optimization. The optimal operating policy also indicates that a partly conflicting relation exists between minimizing the risk of groundwater contamination and maximizing the net benefits of the agricultural activities. Hence, the focus of this paper is at finding the better and better Pareto-fronts in the objective space while dealing with the parts of the objective functions with less conflict to reach the optimal Pareto-front on which the full conflict between the objectives is held. Then, an entropy-based trade-off reflected in designating a couple of weights assigned to the couple of objectives calculated for each solution in the bi-objective space is held over the solutions lying on the optimal Pareto-front and finally, the favorite solution minimizing the weighted-distance to the ideal point in the objective space is achieved using the TOPSIS method. With this policy the regional nitrate concentration will be decreased by 36.7%, 20.45% and 21.6% in the first, second and third study sub-areas, respectively, as compared to those in the actual operation. Furthermore, the model suggests 15%, 12% and 9% wastewater treatment and also 9%, 6% and 7% decrease in the fertilizer use in the first, second, and third study sub-areas, respectively.

Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2, RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.