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A significant role in educational assessment is played by measuring behavioural changes based on clearly specified educational objectives and meaningfully interpreting the results. One of the most significant strategies for evaluating a student's learning performance in the classroom is formative assessment. Formative assessment systems concurrently improve the science and art of teaching and learning. Teachers can help the students grow as students by actively hopeful them to self-assess their skills, test their knowledge retention, giving clear commands and providing the necessary feedback to put them on track. This paper aims to demonstrate the various Formative Assessment Tools for Effective Classroom. The conclusions derived from the teaching fields are then merged to produce an incorporated summing with clear implications for effective didactic academic performance. The assessment of students by the teachers has a powerful direct impact on their learning performance, which may be positive and thus be worthy of very attractive and fruitful learning.

Visually challenged students' career choice is influenced by many factors, including life context, personal aptitudes, and educational attainment. This study focuses the defense styles of visually challenged students and also study about their career choice. Survey method has been adopted in this investigation. Totally 77 samples were collected randomly from 11 colleges. The population of the study is visually challenged students at Undergraduate level of Arts and Science and B.Ed. colleges in Chennai District, Tamilnadu, India. The results reveal that the defense styles of visually challenged students studying at the undergraduate level have influence over the teaching and training career.

Signals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s) distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM). HMM is a three-step process, where the endmembers are first derived from the images themselves using the N-FINDR algorithm. These endmembers are used by the linear mixture model (LMM) in the second step that provides an abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual ground proportions are fed into neural network based multi-layer perceptron (MLP) architecture as input to train the neurons. The neural output further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. HMM is first implemented and validated on simulated hyper spectral data of 200 bands and subsequently on real time MODIS data with a spatial resolution of 250 m. The results on computer simulated data show that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089 ± 0.0022 with LMM and 0.0030 ± 0.0001 with the HMM when compared to actual class proportions. The unmixed MODIS images showed overall RMSE with HMM as 0.0191 ± 0.022 as compared to the LMM output considered alone that had an overall RMSE of 0.2005 ± 0.41, indicating that individual class abundances obtained from HMM are very close to the real observations.

Land cover (LC) refers to the physical and biological cover present over the Earth’s surface in terms of the natural environment such as vegetation, water, bare soil, etc. Most LC features occur at finer spatial scales compared to the resolution of primary remote sensing satellites. Therefore, observed data are a mixture of spectral signatures of two or more LC features resulting in mixed pixels. One solution to the mixed pixel problem is the use of subpixel learning algorithms to disintegrate the pixel spectrum into its constituent spectra. Despite the popularity and existing research conducted on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of several subpixel learning algorithms based on least squares, sparse regression, signal–subspace and geometrical methods. Analysis of the results obtained through computer-simulated and Landsat data indicated that fully constrained least squares (FCLS) outperformed the other techniques. Further, FCLS was used to unmix global Web-Enabled Landsat Data to obtain abundances of substrate (S), vegetation (V) and dark object (D) classes. Due to the sheer nature of data and computational needs, we leveraged the NASA Earth Exchange (NEX) high-performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into four classes, namely forest, farmland, water and urban areas (in conjunction with nighttime lights data) over California, USA using a random forest classifier. Validation of these LC maps with the National Land Cover Database 2011 products and North American Forest Dynamics static forest map shows a 6% improvement in unmixing-based classification relative to per-pixel classification. As such, abundance maps continue to offer a useful alternative to high-spatial-resolution classified maps for forest inventory analysis, multi-class mapping, multi-temporal trend analysis, etc.

Intelligent Intrusion Detection System (IIDS) for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a firewall. The efficiency of IIDS highly relies on the algorithm performance. The enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these algorithms. Here, a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework (SILF), is proposed to learn the attack features and reduce the dimensionality. It also reduces the testing and training time effectively and enhances Linear Support Vector Machine (l-SVM). It constructs an auto-encoder method, an efficient learning approach for feature construction unsupervised manner. Here, the inclusive certified signature (ICS) is added to the encoder and decoder to preserve the sensitive data without being harmed by the attackers. By training the samples in the preliminary stage, the selected features are provided into the classifier (lSVM) to enhance the prediction ability for intrusion and classification accuracy. Thus, the model efficiency is learned linearly. The multi-classification is examined and compared with various classifier approaches like conventional SVM, Random Forest (RF), Recurrent Neural Network (RNN), STL-IDS and game theory. The outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy, precision, recall, F-measure, pvalue, MCC and so on. The proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion detection. Here, the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches. [ABSTRACT FROM AUTHOR]

A novel class of hybrid 4-anilinoquinoline triazines have been synthesized and evaluated in vitro for their antimalarial activity against CQ-sensitive 3D7 strain of P. falciparum as well as for their cytotoxicity toward VERO cell line. Five compounds (19, 20, 23, 41 and 45) exhibited the antimalarial potency superior to CQ. Compounds 14 and 16 were found to be orally active at a dose of 100 mg/kg×4 days against CQ-resistant strain of P. yoelii. Inhibition of β-hematin formation assay and molecular docking study has been conducted in order to gain insight into the mechanism of action of proposed targets for the 4-anilinoquinoline and triazine moiety of the hybrid compounds., (Copyright © 2010 Elsevier Masson SAS. All rights reserved.)

Despite emergence of resistance to CQ and other 4-aminoquinoline drugs in most of the endemic regions, research findings provide considerable support that there is still significant potential to discover new affordable, safe, and efficacious 4-aminoquinoline antimalarials. In present study, new side chain modified 4-aminoquinoline derivatives and quinoline-acridine hybrids were synthesized and evaluated in vitro against NF 54 strain of Plasmodium falciparum. Among the evaluated compounds, compound 17 (MIC=0.125 μg/mL) was equipotent to standard drug CQ (MIC=0.125 μg/mL) and compound 21 (MIC=0.031 μg/mL) was four times more potent than CQ. Compound 17 showed the curative response to all the treated swiss mice infected with CQ-resistant N-67 strain of Plasmodium yoelii at the doses 50 mg/kg and 25 mg/kg for four days by intraperitoneal route and was found to be orally active at the dose of 100 mg/kg for four days. The promising antimalarial potency of compound 17 highlights the significance of exploring the privileged 4-aminoquinoline class for new antimalarials., (Copyright © 2010. Published by Elsevier Ltd.)