New approach to predicting proconvulsant activity with the use of Support Vector Regression

Use of support vector regression in structural optimization: Application to vehicle crashworthiness design

Fault location identification is an important task to provide reliable service to the customer. Most existing artificial intelligence methods such as neural network, fuzzy logic, and support vector machine (SVM) focus on identifying the fault type, section, and distance separately. Furthermore, studies on fault type identification are focused on overhead transmission systems and not on underground distribution systems. In this paper, a fault location method in the distribution system is proposed using SVM, addressing the limitations of existing methods. Support vector classification (SVC) and regression analysis are performed to locate the fault. The method uses the voltage sag data during a fault measured at the primary substation. The type of fault is identified using SVC. The fault resistance and the voltage sag for the estimated fault resistance are identified using support vector regression (SVR) analysis. The possible faulty sections are identified from the estimated voltage sag data and ranked using the Euclidean distance approach. The proposed method identifies the fault distance using SVR analysis. The performance of the proposed method is analyzed using Malaysian distribution system of 40 buses. Test results show that the proposed method gives reliable fault location.

Software cost estimation is the process of predicting the effort required to develop a software system. The basic input for the software cost estimation is coding size and set of cost drivers, the output is Effort in terms of Person-Months (PM’s). Here, the use of support vector regression (SVR) has been proposed for the estimation of software project effort. We have used the COCOMO dataset and our results are compared to Intermediate COCOMO as well as to MOPSO model results for this dataset. It has been observed from the simulation that SVR outperforms other estimating techniques. This paper provides a comparative study on support vector regression (SVR), Intermediate COCOMO and Multiple Objective Particle Swarm Optimization (MOPSO) model for estimation of software project effort. We have analyzed in terms of accuracy and Error rate. Here, data mining tool Weka is used for simulation

On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression

This study aimed to extract apigenin and luteolin from celery seeds using 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) based ultrasonic-assisted technique. Optimal extraction conditions were determined through the integration of support vector regression combined with various optimization algorithms, including Genetic Algorithm, Particle Swarm Optimization, Gray Wolf Optimizer, Bat Algorithm, Firefly Algorithm, and Cuckoo Search Algorithm. The identified optimal conditions included an extraction time of 52 min, an HP-β-CD concentration of 24 mg/mL, and a solvent-to-solid ratio of 24 mL/g. Under these conditions, the predicted apigenin and luteolin content in the extract were 0.156 and 0.814 mg/g, respectively, closely aligning with the actual values of 0.158 ± 0.005 mg/g and 0.807 ± 0.008 mg/g. Notably, the HP-β-CD/water extract exhibited significantly higher concentrations of active ingredients and antioxidant activity compared to the water extract, with the content of apigenin and luteolin in the water extract being only 25 and 45%, respectively, compared to the HP-β-CD/water extract. Regarding the antioxidant effect, the water extract displayed a 50% scavenging capacity (SC50) of 331.29 ± 20.5 g/mL, whereas the SC50 for the HP-β-CD/water extract was 89.04 ± 4.70 g/mL. Molecular modeling and IR spectra analysis also suggested the formation of inclusion complexes between HP-β-CD and apigenin/luteolin. These findings suggested that HP-β-CD could be a preferable alternative to conventional solvents due to its advantages, such as easier control, safety, and cost-effectiveness. In addition, the use of support vector regression and nature-inspired optimization algorithms could be promising alternatives to traditional response surface methodology.

In this paper machine-learning (ML) dependent digital-predistortion (DPD) methodology is presented that overcomes the impairments of signal and non-linearities in Optical Front-hauls with the use of Radio over Fiber (RoF) systems. Volterra methods have been realized in the past for this purpose, however, they are not straight forward while classical artificial networks necessitate the optimum arrangement. The proffered support vector regression (SVR) method using grid search lessens the issues in Volterra based DPD and traditional artificial neural networks techniques In this work, the experimental evaluation for Long Term Evolution 20 MHz, 256 quadrature amplitude modulated signal using 850 nm Multi Mode Vertical cavity surface emitting lasers and Multi-Mode Fiber is linearized using SVR based DPD. The experimental analysis finds that SVR-DPD gathers the RoF non-linearities hence proving to be extraordinarily robust.

Phosphorus (P) is widely known as a limiting nutrient of water eutrophication for inland freshwater ecosystems. Owing to the complexity of P chemistry, remote sensing detection of total phosphorus (TP) concentrations currently remains limited especially for optically complex turbid inland waters. To address this need, a new TP remote sensing algorithm is developed based on prior water optical classification and the use of support vector regression (SVR) machine. The in situ observed datasets, used in this study, were collected at specific times during 2009 ~ 2011, covering a total of 232 stations from eight cruises in Lakes Taihu, Chaohu, Dianchi, and Three Gorges reservoir of China. Three types of waters were first classified by using a recently developed NTD675 (Normalized Trough Depth of spectral reflectance at 675 nm) water classification method. Then, spectral regions sensitive specifically to each water type were explored and expressed via several band ratios and used for retrieval algorithm development. The established type-specific SVR algorithms yield relatively high predictive accuracies. Specifically, the mean absolute percentage errors (MAPE) produced with the independent validation samples were achieved at 32.7, 23.2, and 14.1 % for type 1, type 2, and type 3 waters, respectively. Such water type-specific SVR algorithms are more accurate for the classified waters than an aggregated SVR algorithm for the nonclassified water and also superior to commonly used statistical algorithms. Moreover, application of the developed algorithms with HJ1A/HSI image data demonstrates that the algorithms have a large potential for remote sensing estimation of TP concentrations in optically complex turbid inland waters.

The rod ejection accident (REA) is the design-basis reactivity initiated event and an important aspect for a pressurized water reactor (PWR). The consequence of REA is that it introduces a large positive reactivity insertion in a core, which leads to a fast large power excursion and other parameters changing. Thus, it is important to understand the uncertainty in the parameters of reactor core when REA happens. This paper applies support vector regression (SVR) to analyze accident scenarios with control rod ejection. SVR is an approach based on machine learning and soft computing. SVR, by definition, is an application of support vector machine (SVM) to nonlinear regression problem. Furthermore, the objective of this paper is to train SVR model to identify both safe and potentially unsafe power plant conditions based on real time plant data. The data is obtained from computer generated accident scenarios and is divided into two datasets, training datasets and test datasets. The training dataset are used to train the SVR model and the test dataset are used to test the validation of this model. And then the results obtained by SVR model are compared with that of artificial neural network (ANN) model. The comparison results show that SVR model has superior performance over ANN model and agree well with the general understanding. Because the proposed methodology achieve accurate results, it is likely to be suitable for other data processing of nuclear engineering.

New Data Contradict FDA Findings on Epilepsy Drugs and Suicide

Interactions of aliskiren, a direct renin inhibitor, with antiepileptic drugs in the test of maximal electroshock in mice

Phenobarbital has been in clinical use as an antiepileptic drug (AED) since 1912. The initial clinical success of phenobarbital and other barbiturates affected the design of subsequent AEDs (e.g., phenytoin, primidone, ethosuximide), developed between 1938 and 1962, the chemical structures of which resemble that of phenobarbital. However, the empirical discovery of carbamazepine (1962) and the serendipitous discovery of valproic acid (1967) led to subsequent AEDs having chemical structures that are diverse and completely different from that of phenobarbital. Sixteen AEDs were introduced between 1990 and 2012. Most of these AEDs were developed empirically, using mechanism-unbiased anticonvulsant animal models. The empirical nature of the discovery of these AEDs, coupled with their multiple mechanisms of action, explains their diverse chemical structures. The antiepileptic market is therefore crowded. Future design of new AEDs must have a potential for treating nonepileptic central nervous system (CNS) disorders (e.g., bipolar disorder, neuropathic pain, migraine prophylaxis, or restless legs syndrome). The barbiturates were once used as sedative-hypnotic drugs, but have been largely replaced in this role by the much safer benzodiazepines. In contrast, phenobarbital is still used worldwide in epilepsy. Nevertheless, the development of nonsedating phenobarbital derivatives will answer a clinical unmet need and might make this old AED more attractive.

An approach of a mean hourly wind speed forecasting in wind farm is proposed in this paper. It applies support vector regression as well as forecasting error estimation. Firstly, support vector regression is applied to the mean hourly wind speed forecasting. Secondly, a support vector classifier is trained to estimate the forecasting error. Finally, the forecasting results can tailor themselves to the estimated forecasting error, and thus improve the forecasting precision. To test the approach, three-year data from a wind farm is given as a support vector regression process, and a support vector classifier is trained in addition to estimate the forecasting error. Experimental results show that the proposed approach can achieve higher quality of mean hourly wind speed forecasting; also it has lower mean square error compared with the traditional support vector regression forecasting.

Ensuring reliable inspection and quality control in complex industrial settings remains a significant challenge, particularly when traditional manual methods are applied to dynamic, multi-object environments. This paper presents and validates a new hybrid framework that integrates Object-Centric Process Mining (OCPM) with Support Vector Machines (SVMs) to improve industrial safety and quality assurance. The aims are: (1) to uncover and model the complex, multi-object processes characteristic of modern manufacturing using OCPM; (2) to assess these models in terms of conformance, performance, and the detection of bottlenecks; and (3) to design and embed a predictive layer based on Support Vector Regression (SVR) to anticipate process outcomes and support proactive control.The proposed methodology comprises a comprehensive pipeline: data fusion and OCEL structuring, OCPM for process discovery and conformance analysis, feature engineering, SVR for predictive modeling, and a multi-objective optimization layer. By applying this framework to a timber sawmill dataset, the study successfully modeled complex lumber drying operations, identified key object interactions, achieving a process conformance fitness score of 0.6905, and testing the integration of a predictive SVR layer. The SVR model’s predictive accuracy for production yield was found to be limited (R2=0.0255) with the current feature set, highlighting the challenges of predictive modeling in this complex, multi-object domain. Despite this predictive limitation, the multi-objective optimization effectively balanced defect rates, energy consumption, and process delays, yielding a mean objective function value of 0.0768. These findings demonstrate the framework’s capability to provide deep, object-centric process insights and support data-driven decision-making for operational improvements in Industry 4.0. Future research will focus on improving predictive model performance through advanced feature engineering and exploring diverse machine learning techniques.

Background: Hydro ethanolic extract of Coccinia grandis leaves was assumed to have anticonvulsant activity. Objective: To evaluate the anticonvulsant activity of Coccinia grandis by using some animal models like maximal electric shock model, pentylenetetrazole induced seizures, Isoniazid induced seizures. Materials and Methods: Various equipment and apparatus were used in the present study. Hydroethanolic extract of Coccinia grandis was done by Soxhlet apparatus. Phytochemical investigation of the leaf extract of Coccinia grandis was performed. Anticonvulsant activity was determined by using different animal models like maximal electric shock model, pentylenetetrazole induced seizures, Isoniazid induced seizures. Results: Anticonvulsant activity was determined by using different animal models like Maximal electric shock model, pentylenetetrazole induced seizures & Isoniazid induced model. Percentage of protection, Duration of hind limb tonic extension (HLTE), Latency of onset of convulsions were taken as a indication of anticonvulsant activity. Phenytoin and Diazepam showed significant anticonvulsant activity. In the present study the test samples exhibited significant (P < 0.0001) anticonvulsant activity and antioxidant activity at a dose of 200,400mg/kg. It may due to the presence of alkaloids, Phenols, carbohydrates, glycosides, saponins and flavonoids terpenoids. Conclusion: In present study leaves of Coccinia grandis extract was collected and anticonvulsant activity was examined in a doses of 200mg/kg, 400mg/kg. The results clearly shown that the low dose 200mg/kg of plant extract shown appropriate anticonvulsant activity and the high dose 400mg/kg shown specific anticonvulsant activity when compared to control and standard groups.

Aim : to study the anticonvulsant and psychotropic activities and side effects of pufemid, pyrathidine and compound N3012 developed at the Institute of Fine Organic Chemistry of the National Academy of Sciences. Materials and methods . Anticonvulsant activities were tested using the following models of experimental seizures: maximum electric shock, corazol, camphor, thiosemicarbazide, picrotoxin, strychnine, amigdala-kindling convulsions. The psychotropic activities of the tested compounds were investigated using the “elevated plus maze”, “electroshock retrograde amnesia”, and the “open field” models. The neurotoxicity of the compounds was assessed by the level of impaired motor coordination in the “rotating rod” test and in the “grid climbing” test. Results . Pufemid, pyrathidine, and compound N3212 demonstrated high anticonvulsant activity that was superior to the reference antiepileptic drugs; in addition, the tested compounds had low toxicity and high indicators of therapeutic and protective effects. Pufemid, pyrathidine and compound N3212 showed psychotropic activities: e. g., compound N3212 had anxiolytic and nootropic effects. Conclusion . The tested compounds – pufemid, pyrathidine and compound N3212 – are highly efficient and low-toxic substances and are, therefore, promising for further preclinical and clinical studies as anti-epileptic drugs with psychotropic activities.