Optimal Machine Learning Enabled Performance Monitoring for Learning Management Systems

Learning Management System (LMS) is an application software that is used in automation, delivery, administration, tracking, and reporting of courses and programs in educational sector. The LMS which exploits machine learning (ML) has the ability of accessing user data and exploit it for improving the learning experience. The recently developed artificial intelligence (AI) and ML models helps to accomplish effective performance monitoring for LMS. Among the different processes involved in ML based LMS, feature selection and classification processes find beneficial. In this motivation, this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring (GSO-MFWELM) technique for LMS. The key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in LMS. The proposed GSO-MFWELM technique involves GSO-based feature selection technique to select the optimal features. Besides, Weighted Extreme Learning Machine (WELM) model is applied for classification process whereas the parameters involved in WELM model are optimally fine-tuned with the help of Mayfly Optimization (MFO) algorithm. The design of GSO and MFO techniques result in reduced computation complexity and improved classification performance. The presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several aspects. The simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.     

Architectural space planning using evolutionary computing approaches: a review

This paper presents various applications of evolutionary computing approach for architectural space planning problem. As such the problem of architectural space planning is NP-complete. Finding an optimal solution within a reasonable amount of time for these problems is impossible. However for architectural space planning problem we may not be even looking for an optimal but some feasible solution based on varied parameters. Many different computing approaches for space planning like procedural algorithms, heuristic search based methods, genetic algorithms, fuzzy logic, and artificial neural networks etc. have been developed and are being employed. In recent years evolutionary computation approaches have been applied to a wide variety of applications as it has the advantage of giving reasonably acceptable solution in a reasonable amount of time. There are also hybrid systems such as neural network and fuzzy logic which incorporates the features of evolutionary computing paradigm. The present paper aims to compare the various aspects and merits/demerits of each of these methods developed so far. Sixteen papers have been reviewed and compared on various parameters such as input features, output produced, set of constraints, scope of space coverage-single floor, multi-floor and urban spaces. Recent publications emphasized on energy aspect as well. The paper will help the better understanding of the Evolutionary computing perspective of solving architectural space planning problem. The findings of this paper provide useful insight into current developments and are beneficial for those who look for automating architectural space planning task within given design constraints.     

Hierarchical ATPG for analog circuits and systems

Automatic test-pattern generation (ATPG) algorithms for analog circuits have been under intense investigation for the last several years. As system design aggressively moves to system-on-a-chip (SoC) and core-based integration, hierarchical analog ATPG emerges as an even more difficult challenge. Attempts to develop an effective algorithm have had varying degrees of success. This article reviews some fundamental issues and recent work in hierarchical analog ATPG and presents an algorithm based on controllability and observability computation. This algorithm has been implemented in a prototype tool, and results based on several case studies show the application of the technique     

External stimuli response on a novel chitosan hydrogel crosslinked with formaldehyde

Keeping in mind the significance of hydrogels as an external stimuli sensitive super absorbing material, some transparent covalent hydrogels of chitosan were prepared by crosslinking with varying amounts of formaldehyde solution used as crosslinking agent. The characteristics of hydrogels were investigated by Fourier transform infrared (FT-IR) spectroscopy and swelling experiments. The effect of crosslinking agent on water absorbency has been investigated. The hydrogels exhibited a relatively higher swelling ratio in the range of 2066–3306% and equilibrium water content (EWC) in the range of 95-38–97 06% at pH 7 and 35°C temperature. The influence of external stimuli such as pH, temperature, and ionic strength of the swelling media on equilibrium swelling properties has been observed. Hydrogels showed a typical pH and temperature responsive behaviour such as low pH and high temperature has maximum swelling while high pH and low temperature show minimum swelling. An increase in the ionic strength of swelling media caused a continuous decrease in the swelling of hydrogels at both acidic and basic pH.     

Microstructure and mechanical properties of Al–2Mg alloy base short steel fiber reinforced composites prepared by vortex method

Fabrication and characterization of cast Al–2Mg alloy matrix composites reinforced with short steel fibers are dealt with in the present study. Three types of steel fiber were used: uncoated, copper coated and nickel coated. All the composites were prepared by the liquid metal route using vortex methods. When tested in tension, all composites exhibited improvement in strength due to high relative strength of steel fibers. The ductility was lowered except for the composite with copper coated fibers. Copper coated fiber reinforced composites gave the highest strength. Higher strength accompanied with appreciable ductility demonstrated by composites with copper coated fibers is attributed to the solid solution and fiber strengthening as well as good bonding at the interface. Composites reinforced with uncoated and Ni coated steel fibers did not exhibit strengthening to the level exhibited with copper coated fibers because brittle intermetallic phases are formed at the interface. These phases promote initiation and facilitate propagation of cracks. The observed fracture mechanism of composites was dimple formation, fiber breakage and pullout of fibers. Fracture surface of uncoated and Ni coated composites showed extensive pull out of fibers as well as fiber breakage confirming the above inference. In case of the copper coated composites dimple formation and coalescence was more extensive. EDX analysis showed a build up Cu, Ni, and Fe at the interface.     

Fracture experiments on through wall cracked elbows under in-plane bending moment: Test results and theoretical/numerical analyses

Fracture assessment of pipe bends or elbows with postulated through wall crack is very essential for leak-before-break qualification of primary heat transport system piping of nuclear power plants. The methodology for fracture assessment of cracked elbows is still in developing stage. Any new development in theoretical aspect requires experimental validation. However, fracture test data on cracked elbows is not so abundant as straight pipes. The earlier experiments on cracked elbows were focused mainly on the determination of limit load. Other fracture parameters e.g. crack growth, crack initiation load or crack opening displacement were not reported in the open literature. Against this backdrop, a comprehensive experimental and theoretical program on component integrity has been initiated at Reactor Safety Division (RSD) of Bhabha Atomic Research Center (BARC), India. Under this program, a number of fracture tests have been carried out on elbows with through wall circumferential/axial cracks subjected to in-plane closing/opening bending moment. These test data are then thoroughly analysed numerically through non-linear finite element analyses, analytically through limit load comparison and also through comparison of crack initiation loads by finite element and R6 methods. These test data may be utilized in future for validation of new theoretical developments in the integrity assessment of through wall cracked elbows.     

Heterogeneous object modeling through direct face neighborhood alteration

Two recent advances—the use of functionally gradient materials in parts and layered manufacturing technology—have brought to the forefront the need for design and fabrication methodologies for heterogeneous objects. However, current solid modeling systems, a core component of computer-aided design and fabrication tools, are typically purely geometry based, and only after the modeling of product geometry, can a part's non-geometric attributes such as material composition be modeled. This sequential order of modeling leads to unnecessary operations and over-segmented 3D regions during heterogeneous object modeling processes.

To enable an efficient design of heterogeneous objects, we propose a novel method, direct face neighborhood operation. This approach combines the geometry and material decisions into a common computational framework as opposed to separate and sequential operations in existing modeling systems. We present theories and algorithms for direction face neighborhood alteration, which enables direct alteration of face neighborhood before 3D regions are formed. This alteration is based on set membership classification (SMC) and region material semantics. The SMC is computationally enhanced by the usage of topological characteristics of heterogeneous objects. After the SMC, boundary evaluation is performed according to the altered face neighborhood. In comparison with other solid modeling methods, the direct face neighborhood alteration method is computationally effective, allows direct B-Rep operations, and is efficient for persistent region naming. A prototype system has been implemented to validate the method and some examples are presented.     

Laser composite surfacing of AISI 304 stainless steel with titanium boride for improved wear resistance

The present study concerns development of a hard in situ boride-dispersed composite layer on the surface of AISI 304 stainless steel substrate to improve the wear resistance property. Laser processing was carried out by melting the surface of sand-blasted AISI 304 stainless steel substrate using a continuous wave CO₂ laser and simultaneous deposition of a mixture of K₂TiF₆ (potassium titanium hexafluoride) and KBF₆ (potassium hexafloroborate) (in the weight ratio of 2:1) using Ar as shrouding environment. Powder feed rate was maintained constant at 4 g/min. Irradiation results in dissociation of a pre-deposited mixture along with a part of the stainless steel substrate, intermixing and rapid solidification to form the composite layer on the surface. The micro-structure of composite layer consists of dispersion of titanium boride particles in AISI 304 stainless steel matrix. Volume fraction of particles is found to be uniform throughout the composite layer, though varied with laser parameters. The micro-hardness of the surface was improved 250–350 VHN as compared to 220 VHN of the AISI 304 stainless steel substrate with a significant improvement in wear resistance property. The mechanism of wear was found to be a combination of adhesive and abrasive in as-received stainless steel. However, it was predominantly abrasive for laser composite surfaced stainless steel.