Fuzzy- MCS algorithm based Ontology generation for E Assessment

In this paper, optimal load dispatch problem under competitive electric market (OLDCEM) is solved by the combination of cuckoo search algorithm (CSA) and a new constraint handling approach, called modified cuckoo search algorithm (MCSA). In addition, we also employ the constraint handling method for salp swarm algorithm (SSA) and particle swarm optimization algorithm (PSO) to form modified SSA (MSSA) and modified PSO (MPSO). The three methods have been tested on 3-unit system and 10-unit system under the consideration of payment model for power reserve allocated, and constraints of system and generators. Result comparisons among MCSA and CSA indicate that the proposed constraint handling method is very useful for metaheuristic algorithms when solving OLDCEM problem. As compared to MSSA, MPSO as well as other previous methods, MCSA is more effective by finding higher total benefit for the two systems with faster manner and lower oscillations. Consequently, MCSA method is a very effective technique for OLDCEM problem in power systems.

The use of multiple-choice questions (MCQs) in law schools has not gained widespread acceptance, unlike in medical schools where they enjoy global usage. Law Schools traditionally use essay-type/problem-solving questions to assess students. The efficacy of this form of assessment is increasingly being attacked due to increasing numbers of students and advancements in technology that enable students to generate answers. Bloom’s Taxonomy provides for a hierarchy of learning processes, which include ‘remember’, ‘understand’, ‘apply’, ‘analysis’, ‘evaluate’, and ‘create’. Studies on whether MCQs can test higher-order learning processes required in law courses have been inconclusive. We did a retrospective study to investigate whether MCQs are an efficient and effective way of assessing law courses. Underlying this study was the desire to find an alternative mode of assessment to overcome the threats facing the essay type. Results from selected law courses were analyzed in which students’ performance on MCQ tests, oral tests, and final examinations were compared. MCQs were analyzed on a two-dimensional Bloom’s table to establish the extent to which they tested higher-order learning processes. We compared the results of scores on the MCQs with those of oral tests and final essay-type examinations using correlational analysis and one-way analysis of variance (ANOVA). This was for the courses Constitutional Law (n = 22, MCQs = 75), Intellectual Property Rights (n = 113, MCQs = 100), Broadcast & TV Law (n = 11, MCQs = 76), Administrative Law (n = 65, MCQs = 91), and Cyberspace Law (n = 28, MCQs = 101). In general, students performed best on the MCQ test compared to orals and final exams. A two-tailed correlation analysis comparing all five courses showed a strong correlation between MCQs and Orals (r = 0.699, p = 0.189) and a weak correlation between MCQ test and final exam (r = 0.196, p = 0.752). Sixty-eight percent of 75 MCQs in Constitutional Law tested higher-order processes. The mean scores for MCQ (+ = 39.45 + 4.83), orals (+ = 7.64+18.72), and final exam (+ 28.5 + 8.88) showed a best performance for MCGs. ANOVA comparing test scores for MCQs, Orals and Final examination showed a very significant difference (F (2, 63, 0.05) = 38.11, p < 0.0001). Fifty-four out of 100 MCQs for intellectual property Law tested higher-order learning processes. The mean scores for MCQ (+ = 39.78 + 5.22), orals (+ = 18.88+14.66), and final exam (+ 29.0442 + 7.71) showed a best performance for MCQs. ANOVA results were highly significant (F (2,342,0.05) = 125.565, p < 0.0001). In Broadcast & TV Law, 44.7% of 76 MCQs tested higher-order learning processes. The mean scores for MCQ (+ = 43.27+6.89), orals (+ = 31.09+12.53), and final exam (+ 32.09 + 6.69) were different. ANOVA results were very significant (F (2,30,0.05) = 6.057, p < 0.01). There were 91 MCQs in Administrative Law of which 74% tested higher-order processes. The mean scores for the three tests were MCQ (+ = 33.05 + 4.76), orals (+ = 22.89 + 16.08), and final exam (+ 32.58 + 5.58). ANOVA results were very significant with F (2,191,0.05) = 20.388, p < 0.0001. Cyberspace Law had 101 MCQs of which 38.6% tested higher-order learning processes. The mean scores for MCQ (+ = 43.50 + 4.51), orals (+ = 36.43 + 2.27), and final exam (+ 35.04 + 7.71) were different. ANOVA results were very significant (F (2,81,0.05) = 20.375, p < 0.0001). We concluded that MCQs are efficacious and efficient in testing higher-order learning processes. MCQs can be used to assess learning of law courses. We recommend that law schools should embrace MCQs for assessing law courses.

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.

Methodological problems with previous comparison of multiple-choice (MC) and true-false (TF) tests include differential item improvement, nonequivalence of stimuli, and restricted range for the TF form. This paper compares reliabilities and concurrent validities of tests composed of unimproved, teacher-made items. Form equivalence was assured by using every MC option as a TF item; range was considered comparatively unrestricted since the TF test took longer to complete than the MC test. No significant differences were found in either reliability or validity even when the MC reliability was adjusted to equate testing time. These results provide support for the use of TF tests as alternatives to MC tests in objective testing and are discussed in relation to previous findings.

The Office of Academic Computing at the Case Western Reserve University School of Medicine (Case) has created a web‐based eAssessment system to administer secure computer‐based examinations, quizzes, and exercises. The system has been used at Case since 1999 to administer formative and summative assessments throughout the 4 year curriculum. The system (currently .NET) allows faculty to create, score, and analyze multiple choice or short answer questions anywhere/any time via a secure SQL server. Questions can be chosen from a database or constructed new. Faculty control the assessment time, password, and grading. Each question can contain case scenarios, color illustrations, sound files, video clips, Flash animations, and virtual microscope slides. A control panel allows students to move between questions and mark them for review. Analysis of student performance is available to both faculty and students (via an ePortfolio) immediately after the assessment. Because the system is web based, it can be used by faculty and students anywhere as long as they have high speed access to the Internet. The system was successfully used in a graduate course at the Medical University of Ohio (MUO) to administer 6 summative assessments averaging 50 multiple choice questions each to students using notebook computers in a wireless environment. The 120 mile distance between Case and MUO had NO impact on either faculty creating and administering the assessments or students taking them.

A stable agricultural economy, food security, and quality of life largely depend on efficient plant Pest Detection (PD).Rapid insect and pest infestations severely affect annual crop yields, and existing detection methods often face security and performance limitations.Globally, researchers are exploring scientific approaches that leverage Cloud Computing (CC) systems for pest detection.To address these challenges, several automation and Image Processing (IP)-based strategies have emerged in recent years.This study proposes a Hybrid Deep Learning (HDL) and Improved Feature Weighted Fuzzy Clustering (IFWFC) model for effective Pest Recognition (PR).Initially, preprocessing is performed using Z-Score Normalization (ZSN).The IFWFC process is then introduced, and data security is enhanced through a Henon Chaotic Map Encryption (HCME) technique.For feature selection, the Modified Cuckoo Search Algorithm (MCSA) is employed.Finally, pest recognition is achieved using a hybrid framework that combines HDL with Bidirectional Long Short-Term Memory (Bi-LSTM).In addition, a Faster Region Convolutional Neural Network (FRCNN) is integrated with Bi-LSTM for hybrid classification.To improve the performance further, the Improved Butterfly Optimization Algorithm (IBOA) is applied.Experimental evaluation using the IP102 dataset demonstrates that the proposed model achieves superior accuracy (ACC) compared to conventional Machine Learning (ML) methods, highlighting its effectiveness for automated pest recognition.

The Cuckoo search (CS) algorithm is a simple and efficient global optimization algorithm and it has been applied to figure out large range of real-world optimization problem. In this paper, a new formula is introduced to the discovering probability process to improve the convergence rate and the Tournament Selection Strategy is adopted to enhance global search ability of the certain algorithm. Then an approach for structural damage identification based on modified Cuckoo search (MCS) is presented. Meanwhile, we take frequency residual error and the modal assurance criterion (MAC) as indexes of damage detection in view of the crack damage, and the MCS algorithm is utilized to identifying the structural damage. A simply supported beam and a 31-bar truss are studied as numerical example to illustrate the correctness and efficiency of the propose method. Besides, a laboratory work is also conducted to further verification. Studies show that, the proposed method can judge the damage location and degree of structures more accurately than its counterpart even under measurement noise, which demonstrates the MCS algorithm has a higher damage diagnosis precision.

Automatic assessment of Java code

Fuzzy logic-based approach for identifying the risk importance of human error

Abstract. This study aims to determine whether there are differences in learning outcome is taught by models Contextual Teaching and Learning (CTL) and the model of Guided Inquiry (GI) and to investigate the cognitive level most growth in both classes. Sampling was done by purposive sampling technique by taking two classes of class X MIPA-1 and as the experimental class 1 and class X MIPA 9 as an experimental class 2. The instrument used to determine student learning outcomes is an objective test in the form of multiple choice questions numbered 30 which has proven validity, level of difficulty, power differentiator, distractors, and reliability. R_11 price obtained for the price rtabel = 0.892 and 0.312. overall reliability of the test can be said to be reliable because rhitung> rtabel. Class 1 is given treatment experiments using models Contextual Teaching and Learning and the experimental class 2 given treatment using a model of Guided Inquiry. After the pretest and posttest and data processing of the obtained χ2 Calculate pre-test 0.380 and χ2 Calculate post-test 0.319 for the experimental class 1 and χ2 Calculate pre-test 0.223 and χ2 Calculate post-test 0.107 for the experimental class 2 and value sign α = 0.05. Because χ2 Calculate> sign, then the normal distribution of data. At Fhitung homogeneity test showed a smaller number than the F table, namely 0.248> 0,05untuk value pretest, 0.16> 0.05 for the value posttest and 0.743> 0.05 for the gain so that the data homogeneous. Gain learning outcomes in the experimental class 1 .7756 (75.56%), gain learning outcomes in the experimental class 2 .5640 (56.40%). Based on the results of testing the hypothesis improving student learning outcomes obtained price sign> α ie 0.000> 0.005 with a significance level (α = 0.05) so that Ha is accepted and we can conclude that there are differences in learning outcome is taught by models Contextual Teaching and Learning and Guided models Inquiry. In addition, also obtained an increase in cognitive Siswadi experimental class that is at the level of knowledge (C1) amounted to 54.17%, of understanding (C2) of 78.85%, the application (C3) amounted to 75.62%, and for analysis (C4) amounted to 77.22% and an increase in cognitive students in the experimental class 2, namely the level of knowledge (C1) amounted to 51.67%, of understanding (C2) of 47.93%, the application (C3) amounted to 49.58%, and for analysis (C4) of 65.05%. Keywords : CTL, GI, atomic structure, learning outcomes, SPSS 21 for windows

Applying ontology learning and multi-objective ant colony optimization method for focused crawling to meteorological disasters domain knowledge

— In this paper, two optimization algorithms i.e., Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are combined to produce a hybrid population-based algorithm PSOGSA. The approach of HPSOGSA is to use the assessment of PSO using the social thinking capability and the manipulation of GSA using the local search proficiency. To increase the performance of HPSOGSA, some benchmark functions are used. Two standard database ORL and YALE is used to assess the classification performance of the proposed method. Classification accuracy is compared for diverse number of training samples per class. Further lead of proposed method is confirmed by analyzing percentage improvement in classification accuracy. With limited accessibility of training sample, percentage enhancement is very successful for face recognition applications. Keywords: Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (HPSOGSA). Cite this Article Purvaa Saxena, Gargi Mishra, Apoorva Aggarwal, Ayush Priyadarshi, Nitin Kumar, Rahul. Pattern Recognition Using Hybrid Meta-Heuristic. Journal of Artificial Intelligence Research & Advances. 2019; 6(2): 100–108p.

Centroid mutation-based Search and Rescue optimization algorithm for feature selection and classification

Preparation for a sea voyage is one of the fundamental aspects of navigation. Several complexities are involved during the preparation of the ship for navigation due to the nature of maritime work. At this point, analysing human-related error is of paramount importance to ensure the safety of the ship and the crew. This paper describes the principles of a methodology, namely fuzzy-based shipboard operation human reliability analysis (SOHRA), to quantitatively perform human error assessment through procedures of preparing the ship for navigation. While the SOHRA (a marine-specific HRA approach) quantifies human error, the fuzzy logic deals with ambiguity and vagueness in the human error detection problem. The findings show that the total HEP (Human error probability) is found 1.49E-01 for preparing the ship for navigation. Consequently, the paper provides practical contributions to shore-based safety professionals, ship managers, and masters of the ship since it performs a systematic human reliability assessment and enhances safety control levels in the operational aspect.

ABSTRAK Penelitian ini dimaksudkan untuk mengetahui perbandingan hasil belajar peserta didik pada materi gelombang cahaya antara yang menggunakan media E-learning LMS MOODLE dan Edmodo. Metode penelitian yang digunakan adalah quasi eksperimen dengan desain nonequivalent control group dan teknik pengambilan sampel yaitu simple random sampling. Dalam penelitian ini, terdapat 2 sampel yaitu XI-MIPA 1 (kelas eksperimen 1) dan XI-MIPA 2 (kelas eksperimen 2). Instrumen yang digunakan yaitu tes objektif (soal pilihan ganda) dan nontes (lembar observasi). Berdasarkan hasil uji hipotesis menggunakan statistik parametrik uji T, diperoleh nilai signifikasi (0,001) 0,05. Hasil ini memberikan informasi bahwa terdapat perbedaan hasil belajar peserta didik pada materi gelombang cahaya antara yang menggunakan media e-learning LMS MOODLE dan Edmodo. Selain itu, hasil observasi menunjukkan kelas yang menggunakan media e-learning LMS MOODLE terkategori lebih baik dibandingkan kelas yang menggunakan media e-learnig Edmodo untuk setiap indikator observasi (visual, productivity, communication dan evaluation). Kata kunci: E-learning, LMS MOODLE, Edmodo, Hasil Belajar Peserta Didik. ABSTRACT This research is intended to determine the comparison of student physics learning outcomes between those using E-learning media LMS MOODLE and Edmodo. The research method used was quasi-experimental with nonequivalent control group design and the sampling technique was simple random sampling. In this study there were two samples, namely XI-MIPA 1 as the experimental class 1 and XI-MIPA 2 as the experimental class 2. The instruments used were objective tests (multiple choice questions) and non-test (observation sheets). Based on statistical calculation of posttest by using parametric statistical T test showed tha significant score 2-tailed (0,001) 0,05. It means that there are differences in student physics learning outcomes between those using E-Learning media LMS MOODLE and Edmodo. Besides that, Observations results showed that classes using e-learning media LMS MOODLE were in a better category than classes using e-learning media Edmodo for each observation indicator (visual, productivity, communication and evaluation). Keywords: E-learning, LMS MOODLE, Edmodo, student physics learning outcomes