Ontology alignment evaluation for online assessment of e-learners: a new e-learning management system

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajakumar B.R. ◽  
Gokul Yenduri ◽  
Sumit Vyas ◽  
Binu D.
Keyword(s):  
Hierarchical Clustering ◽  
Optimization Algorithm ◽  
Semantic Processing ◽  
Assessment System ◽  
Online Assessment ◽  
Ontology Alignment ◽  
Content Type ◽  
System Module ◽  
The Mean ◽  
Better Than

Purpose This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades. Findings On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.

Intelligent portfolio asset prediction enabled by hybrid Jaya-based spotted hyena optimization algorithm

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Meeta Sharma ◽  
Hardayal Singh Shekhawat
Keyword(s):  
Deep Learning ◽  
Portfolio Optimization ◽  
Optimization Algorithm ◽  
Spotted Hyena ◽  
Content Type ◽  
Whale Optimization ◽  
The Future ◽  
Asset Selection ◽  
Learning Concept ◽  
Better Than

Purpose The purpose of this study is to provide a novel portfolio asset prediction by means of the modified deep learning and hybrid meta-heuristic concept. In the past few years, portfolio optimization has appeared as a demanding and fascinating multi-objective problem, in the area of computational finance. Yet, it is accepting the growing attention of fund management companies, researchers and individual investors. The primary issues in portfolio selection are the choice of a subset of assets and its related optimal weights of every chosen asset. The composition of every asset is chosen in a manner such that the total profit or return of the portfolio is improved thereby reducing the risk at the same time. Design/methodology/approach This paper provides a novel portfolio asset prediction using the modified deep learning concept. For implementing this framework, a set of data involving the portfolio details of different companies for certain duration is selected. The proposed model involves two main phases. One is to predict the future state or profit of every company, and the other is to select the company which is giving maximum profit in the future. In the first phase, a deep learning model called recurrent neural network (RNN) is used for predicting the future condition of the entire companies taken in the data set and thus creates the data library. Once the forecasting of the data is done, the selection of companies for the portfolio is done using a hybrid optimization algorithm by integrating Jaya algorithm (JA) and spotted hyena optimization (SHO) termed as Jaya-based spotted hyena optimization (J-SHO). This optimization model tries to get the optimal solution including which company has to be selected, and optimized RNN helps to predict the future return while using those companies. The main objective model of the J-SHO-based RNN is to maximize the prediction accuracy and J-SHO-based portfolio asset selection is to maximize the profit. Extensive experiments on the benchmark datasets from real-world stock markets with diverse assets in various time periods shows that the developed model outperforms other state-of-the-art strategies proving its efficiency in portfolio optimization. Findings From the analysis, the profit analysis of proposed J-SHO for predicting after 7 days in next month was 46.15% better than particle swarm optimization (PSO), 18.75% better than grey wolf optimization (GWO), 35.71% better than whale optimization algorithm (WOA), 5.56% superior to JA and 35.71% superior to SHO. Therefore, it can be certified that the proposed J-SHO was effective in providing intelligent portfolio asset selection and prediction when compared with the conventional methods. Originality/value This paper presents a technique for providing a novel portfolio asset prediction using J-SHO algorithm. This is the first work uses J-SHO-based optimization for providing a novel portfolio asset prediction using the modified deep learning concept.

Tasks mapping in the network on a chip using an improved optimization algorithm

2020 ◽  
Vol 16 (2) ◽  
pp. 165-182
Author(s):  
Mehdi Darbandi ◽  
Amir Reza Ramtin ◽  
Omid Khold Sharafi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Computational Time ◽  
Swarm Optimization ◽  
Content Type ◽  
Better Than

Purpose A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose. Design/methodology/approach In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC. Findings The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm. Originality/value As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

Adaptive whale optimization for intelligent multi-constraints power quality improvement under deregulated environment

2019 ◽  
Vol 17 (3) ◽  
pp. 490-514
Author(s):  
Niharika Thakur ◽  
Y.K. Awasthi ◽  
Manisha Hooda ◽  
Anwar Shahzad Siddiqui
Keyword(s):  
Power Quality ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Quality Improvements ◽  
Content Type ◽  
Facts Devices ◽  
Whale Optimization ◽  
Quality Issues ◽  
Better Than ◽  
Bus System

Purpose Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems. Design/methodology/approach This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions. Findings Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively. Originality/value This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

Optimal location of UPQC for power quality improvement: novel hybrid approach

2020 ◽  
Vol 18 (6) ◽  
pp. 1519-1541
Author(s):  
Kaladhar Gaddala ◽  
P. Sangameswara Raju
Keyword(s):  
Cost Function ◽  
Power Quality ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Reactive Power Compensation ◽  
Content Type ◽  
Whale Optimization ◽  
Better Than

Purpose In general, the optimal reactive power compensation could drastically enhance the performance of distributed network by the reduction of power loss and by enhancement of line loadability and voltage profile. Till now, there exist various reactive power compensation models including capacitor placement, joined process of on-load tap changer and capacitor banks and integration of DG. Further, one of the current method is the allocation of distribution FACTS (DFACTS) device. Even though, the DFACTS devices are usually used in the enhancement of power quality, they could be used in the optimal reactive power compensation with more effectiveness. Design/methodology/approach This paper introduces a power quality enhancement model that is based on a new hybrid optimization algorithm for selecting the precise unified power quality conditioner (UPQC) location and sizing. A new algorithm rider optimization algorithm (ROA)-modified particle swarm optimization (PSO) in fitness basis (RMPF) is introduced for this optimal selections. Findings Through the performance analysis, it is observed that as the iteration increases, there is a gradual minimization of cost function. At the 40th iteration, the proposed method is 1.99 per cent better than ROA and genetic algorithm (GA); 0.09 per cent better than GMDA and WOA; and 0.14, 0.57 and 1.94 per cent better than Dragonfly algorithm (DA), worst solution linked whale optimization (WS-WU) and PSO, respectively. At the 60th iteration, the proposed method attains less cost function, which is 2.07, 0.08, 0.06, 0.09, 0.07 and 1.90 per cent superior to ROA, GMDA, DA, GA, WS-WU and PSO, respectively. Thus, the proposed model proves that it is better than other models. Originality/value This paper presents a technique for optimal placing and sizing of UPQC. To the best of the authors’ knowledge, this is the first work that introduces RMPF algorithm to solve the optimization problems.

Gamma knife radiosurgery in the management of cavernous sinus meningiomas

2000 ◽  
Vol 93 (supplement_3) ◽  
pp. 68-73 ◽  
Author(s):  
Pierre-Hugues Roche ◽  
Jean Régis ◽  
Henry Dufour ◽  
Henri-Dominique Fournier ◽  
Christine Delsanti ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Tumor Growth ◽  
Gamma Knife ◽  
Cavernous Sinus ◽  
Gamma Knife Radiosurgery ◽  
Surgical Removal ◽  
Content Type ◽  
Cavernous Sinus Meningioma ◽  
The Mean ◽  
Fine Print

Object. The authors sought to assess the functional tolerance and tumor control rate of cavernous sinus meningiomas treated by gamma knife radiosurgery (GKS). Methods. Between July 1992 and October 1998, 92 patients harboring benign cavernous sinus meningiomas underwent GKS. The present study is concerned with the first 80 consecutive patients (63 women and 17 men). Gamma knife radiosurgery was performed as an alternative to surgical removal in 50 cases and as an adjuvant to microsurgery in 30 cases. The mean patient age was 49 years (range 6–71 years). The mean tumor volume was 5.8 cm3 (range 0.9–18.6 cm3). On magnetic resonance (MR) imaging the tumor was confined in 66 cases and extensive in 14 cases. The mean prescription dose was 28 Gy (range 12–50 Gy), delivered with an average of eight isocenters (range two–18). The median peripheral isodose was 50% (range 30–70%). Patients were evaluated at 6 months, and at 1, 2, 3, 5, and 7 years after GKS. The median follow-up period was 30.5 months (range 12–79 months). Tumor stabilization after GKS was noted in 51 patients, tumor shrinkage in 25 patients, and enlargement in four patients requiring surgical removal in two cases. The 5-year actuarial progression-free survival was 92.8%. No new oculomotor deficit was observed. Among the 54 patients with oculomotor nerve deficits, 15 improved, eight recovered, and one worsened. Among the 13 patients with trigeminal neuralgia, one worsened (contemporary of tumor growing), five remained unchanged, four improved, and three recovered. In a patient with a remnant surrounding the optic nerve and preoperative low vision (3/10) the decision was to treat the lesion and deliberately sacrifice the residual visual acuity. Only one transient unexpected optic neuropathy has been observed. One case of delayed intracavernous carotid artery occlusion occurred 3 months after GKS, without permanent deficit. Another patient presented with partial complex seizures 18 months after GKS. All cases of tumor growth and neurological deficits observed after GKS occurred before the use of GammaPlan. Since the initiation of systematic use of stereotactic MR imaging and computer-assisted modern dose planning, no more side effects or cases of tumor growth have occurred. Conclusions. Gamma knife radiosurgery was found to be an effective low morbidity—related tool for the treatment of cavernous sinus meningioma. In a significant number of patients, oculomotor functional restoration was observed. The treatment appears to be an alternative to surgical removal of confined enclosed cavernous sinus meningioma and should be proposed as an adjuvant to surgery in case of extensive meningiomas.

Gamma knife radiosurgery for craniopharyngiomas

2000 ◽  
Vol 93 (supplement_3) ◽  
pp. 47-56 ◽  
Author(s):  
Wen-Yuh Chung ◽  
David Hung-Chi Pan ◽  
Cheng-Ying Shiau ◽  
Wan-Yuo Guo ◽  
Ling-Wei Wang
Keyword(s):  
Clinical Outcome ◽  
Gamma Knife ◽  
Tumor Volume ◽  
Gamma Knife Radiosurgery ◽  
Tumor Control ◽  
Content Type ◽  
Stereotactic Aspiration ◽  
Cystic Component ◽  
The Mean ◽  
Fine Print

Object. The goal of this study was to elucidate the role of gamma knife radiosurgery (GKS) and adjuvant stereotactic procedures by assessing the outcome of 31 consecutive patients harboring craniopharyngiomas treated between March 1993 and December 1999. Methods. There were 31 consecutive patients with craniopharyngiomas: 18 were men and 13 were women. The mean age was 32 years (range 3–69 years). The mean tumor volume was 9 cm3 (range 0.3–28 cm3). The prescription dose to the tumor margin varied from 9.5 to 16 Gy. The visual pathways received 8 Gy or less. Three patients underwent stereotactic aspiration to decompress the cystic component before GKS. The tumor response was classified by percentage reduction of tumor volume as calculated based on magnetic resonance imaging studies. Clinical outcome was evaluated according to improvement and dependence on replacement therapy. An initial postoperative volume increase with enlargement of a cystic component was found in three patients. They were treated by adjuvant stereotactic aspiration and/or Ommaya reservoir implantation. Tumor control was achieved in 87% of patients and 84% had fair to excellent clinical outcome in an average follow-up period of 36 months. Treatment failure due to uncontrolled tumor progression was seen in four patients at 26, 33, 49, and 55 months, respectively, after GKS. Only one patient was found to have a mildly restricted visual field; no additional endocrinological impairment or neurological deterioration could be attributed to the treatment. There was no treatment-related mortality. Conclusions. Multimodality management of patients with craniopharyngiomas seemed to provide a better quality of patient survival and greater long-term tumor control. It is suggested that GKS accompanied by adjuvant stereotactic procedures should be used as an alternative in treating recurrent or residual craniopharyngiomas if further microsurgical excision cannot promise a cure.

Gamma knife radiosurgery for uveal melanomas: an 8-year experience

2000 ◽  
Vol 93 (supplement_3) ◽  
pp. 184-188 ◽  
Author(s):  
Gerald Langmann ◽  
Gerhard Pendl ◽  
Georg Papaefthymiou ◽  
Helmuth Guss ◽  
Keyword(s):  
Gamma Knife ◽  
Ciliary Body ◽  
Tumor Regression ◽  
Gamma Knife Radiosurgery ◽  
Neovascular Glaucoma ◽  
Content Type ◽  
Prescription Dose ◽  
The Mean ◽  
Fine Print ◽  
Uveal Melanomas

Object. The authors report their experience using gamma knife radiosurgery (GKS) to treat uveal melanomas. Methods. Between 1992 and 1998, 60 patients were treated with GKS at a prescription dose between 45 Gy and 80 Gy. The mean diameter of the tumor base was 12.2 mm (range 3–22 mm). The mean height of the tumor prominence was 6.7 mm (range 3–12 mm). The eye was immobilized. The follow-up period ranged from 16 to 94 months. Tumor regression was achieved in 56 (93%) of 60 patients. There were four recurrences followed by enucleation. The severe side effect of neovascular glaucoma developed in 21 (35%) patients in a high-dose group with larger tumors and in proximity to the ciliary body. A reduction in the prescription dose to 40 Gy or less and excluding treatment to tumors near the ciliary body decreased the rate of glaucoma without affecting the rate of tumor control. Conclusions. Gamma knife radiosurgery at a prescription dose of 45 Gy or more can achieve tumor regression in 85% of the uveal melanomas treated. Neovascular glaucoma can develop in patients when using this dose in tumors near the ciliary body. It is advised that such tumors be avoided and that the prescription dose be reduced to 40 Gy.

Gamma knife radiosurgery for intracranial metastatic melanoma: a 6-year experience

2002 ◽  
Vol 97 ◽  
pp. 494-498 ◽  
Author(s):  
Jorge Gonzalez-martinez ◽  
Laura Hernandez ◽  
Lucia Zamorano ◽  
Andrew Sloan ◽  
Kenneth Levin ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Brain Metastases ◽  
Metastatic Melanoma ◽  
Gamma Knife Radiosurgery ◽  
Tumor Control ◽  
Karnofsky Performance Scale ◽  
Content Type ◽  
Significant Difference ◽  
The Mean ◽  
Fine Print

Object. The purpose of this study was to evaluate retrospectively the effectiveness of stereotactic radiosurgery for intracranial metastatic melanoma and to identify prognostic factors related to tumor control and survival that might be helpful in determining appropriate therapy. Methods. Twenty-four patients with intracranial metastases (115 lesions) metastatic from melanoma underwent radiosurgery. In 14 patients (58.3%) whole-brain radiotherapy (WBRT) was performed, and in 12 (50%) chemotherapy was conducted before radiosurgery. The median tumor volume was 4 cm3 (range 1–15 cm3). The mean dose was 16.4 Gy (range 13–20 Gy) prescribed to the 50% isodose at the tumor margin. All cases were categorized according to the Recursive Partitioning Analysis classification for brain metastases. Univariate and multivariate analyses of survival were performed to determine significant prognostic factors affecting survival. The mean survival was 5.5 months after radiosurgery. The analyses revealed no difference in terms of survival between patients who underwent WBRT or chemotherapy and those who did not. A significant difference (p < 0.05) in mean survival was observed between patients receiving immunotherapy or those with a Karnofsky Performance Scale (KPS) score of greater than 90. Conclusions. The treatment with systemic immunotherapy and a KPS score greater than 90 were factors associated with a better prognosis. Radiosurgery for melanoma-related brain metastases appears to be an effective treatment associated with few complications.

Prolonged survival in a subgroup of patients with brain metastases treated by gamma knife surgery

2005 ◽  
Vol 102 (Special_Supplement) ◽  
pp. 262-265
Author(s):  
C. P. Yu ◽  
Joel Y. C. Cheung ◽  
Josie F. K. Chan ◽  
Samuel C. L. Leung ◽  
Robert T. K. Ho
Keyword(s):  
Brain Metastases ◽  
Gamma Knife ◽  
Patient Survival ◽  
Gamma Knife Surgery ◽  
Prolonged Survival ◽  
Organ Involvement ◽  
Lesion Volume ◽  
Content Type ◽  
The Mean ◽  
Fine Print

Object. The authors analyzed the factors involved in determining prolonged survival (≥ 24 months) in patients with brain metastases treated by gamma knife surgery (GKS). Methods. Between 1995 and 2003, a total of 116 patients underwent 167 GKS procedures for brain metastases. There was no special case selection. Smaller and larger lesions were treated with different protocols. The mean patient age was 56.9 years, the mean number of initial lesions was 3.15, and the mean lesion volume was 10.45 cm.3 The mean follow-up time was 9.2 months. The median patient survival was 8.68 months. One-, 2-, 3-, 4-, and 5-year actuarial survival rates were 31.8%, 19.8%, 14.6%, 7.7%, and 6.9%, respectively. Patient age, number of lesions at presentation, and lesion volume had no influence on patient survival. Twenty-three (19.8%) patients survived for 24 months or more. Certain factors were associated with increased survival time. These were stable primary disease (21 of 23 patients), a long latency between diagnosis of the primary tumor and the occurrence of brain metastases (mean 28.4 months, median 16 months), absence of third-organ involvement, and repeated local procedures. Ten patients underwent repeated GKS (mean 3.4 per patient). Seven patients required open surgery for local treatment failures (recurrence or radiation necrosis). Two patients had both. Fifteen patients underwent repeated procedures. Conclusions. Aggressive local therapy with GKS, repeated GKS, and GKS plus surgery can achieve increased survival in a subgroup of patients with stable primary disease, no third-organ involvement, and long primary-brain secondary intervals.

scholarly journals Buyer Inspired Meta-Heuristic Optimization Algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 194-219 ◽  
Author(s):  
Sanjoy Debnath ◽  
Wasim Arif ◽  
Srimanta Baishya
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Statistical Test ◽  
Heuristic Optimization ◽  
Product Choice ◽  
Exploration And Exploitation ◽  
The Social ◽  
Engineering Problems ◽  
The Mean

AbstractNature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a novel Buyer Inspired Meta-heuristic optimization Algorithm (BIMA) inspired form the social behaviour of human being in searching and bargaining for products. In BIMA, exploration and exploitation are achieved through shop to shop hoping and bargaining for products to be purchased based on cost, quality of the product, choice and distance to the shop. Comprehensive simulations are performed on 23 standard mathematical and CEC2017 benchmark functions and 3 engineering problems. An exhaustive comparative analysis with other algorithms is done by performing 30 independent runs and comparing the mean, standard deviation as well as by performing statistical test. The results showed significant improvement in terms of optimum value, convergence speed, and is also statistically more significant in comparison to most of the reported popular algorithms.

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