Using concept similarity in cross ontology for adaptive e-Learning systems

Abstracte-Learning is one of the most preferred media of learning by the learners. The learners search the web to gather knowledge about a particular topic from the information in the repositories. Retrieval of relevant materials from a domain can be easily implemented if the information is organized and related in some way. Ontologies are a key concept that helps us to relate information for providing the more relevant lessons to the learner. This paper proposes an adaptive e-Learning system, which generates a user specific e-Learning content by comparing the concepts with more than one system using similarity measures. A cross ontology measure is defined, which consists of fuzzy domain ontology as the primary ontology and the domain expert’s ontology as the secondary ontology, for the comparison process. A personalized document is provided to the user with a user profile, which includes the data obtained from the processing of the proposed method under a User score, which is obtained through the user evaluation. The results of the proposed e-Learning system under the designed cross ontology similarity measure show a significant increase in performance and accuracy under different conditions. The assessment of the comparative analysis, showed the difference in performance of our proposed method over other methods. Based on the assessment results it is proved that the proposed approach is effective over other methods

UNMANNED IMAGE NARRATION

Image analysis and understanding is used in various fields such as satellite imaging, robotic technologies, medical imaging, defense[G1]  etc. Image analysis is the process which involves extraction of meaningful information from images.Image annotation is the process of adding metadata to the image. The meaningful information is extracted based on the metadata available with the image. This research aims at understanding the input image and describing it to the user. The input image is segmented. The segments are annotated based on its properties like color, shapes in it. The segment annotations are collected and processed to get the overall meaning of the image. This research would improve image search, inform the user about the image even if the image is not displayed due to compatibility issues, and it is unmanned - no human assistance required to understand and display the image contents. [G2] [G3] Â

Experiencing Company's Popularity and Finding Correlation between Companies in Various Countries Using Facebook's Insight Data

AbstractThe aim of this research was to analyse and experience the various electronics company profiles in various countries using giant social media, Facebook. This analysis was performed with the Insight data of Facebook's page which provide 4 different count values named day, day_28, week and lifetime respectively. To analyze the company's performance in various countries, aggregation was performed to find total users in a country those are engaged with different Facebook pages. All these four counts were used to compare various companies popularity using various measures like Total Country in which people knows about Company, Top-K Country and Least-K country, Count Comparison, Country wise Standard Deviation, Correlation between two companies in a country. Analysis results proved that Samsung was more popular in most of the country compared to all other companies. These findings will definitely help the companies in improving their popularity in social media, which intern will improve their business

Carbonic anhydrase enzymes regulate mast cell–mediated inflammation

Type 2 cytokine responses are necessary for the development of protective immunity to helminth parasites but also cause the inflammation associated with allergies and asthma. Recent studies have found that peripheral hematopoietic progenitor cells contribute to type 2 cytokine–mediated inflammation through their enhanced ability to develop into mast cells. In this study, we show that carbonic anhydrase (Car) enzymes are up-regulated in type 2–associated progenitor cells and demonstrate that Car enzyme inhibition is sufficient to prevent mouse mast cell responses and inflammation after Trichinella spiralis infection or the induction of food allergy–like disease. Further, we used CRISPR/Cas9 technology and illustrate that genetically editing Car1 is sufficient to selectively reduce mast cell development. Finally, we demonstrate that Car enzymes can be targeted to prevent human mast cell development. Collectively, these experiments identify a previously unrecognized role for Car enzymes in regulating mast cell lineage commitment and suggest that Car enzyme inhibitors may possess therapeutic potential that can be used to treat mast cell–mediated inflammation

Methamphetamine Inhibits the Glucose Uptake by Human Neurons and Astrocytes: Stabilization by Acetyl-L-Carnitine

Methamphetamine (METH), an addictive psycho-stimulant drug exerts euphoric effects on users and abusers. It is also known to cause cognitive impairment and neurotoxicity. Here, we hypothesized that METH exposure impairs the glucose uptake and metabolism in human neurons and astrocytes. Deprivation of glucose is expected to cause neurotoxicity and neuronal degeneration due to depletion of energy. We found that METH exposure inhibited the glucose uptake by neurons and astrocytes, in which neurons were more sensitive to METH than astrocytes in primary culture. Adaptability of these cells to fatty acid oxidation as an alternative source of energy during glucose limitation appeared to regulate this differential sensitivity. Decrease in neuronal glucose uptake by METH was associated with reduction of glucose transporter protein-3 (GLUT3). Surprisingly, METH exposure showed biphasic effects on astrocytic glucose uptake, in which 20 µM increased the uptake while 200 µM inhibited glucose uptake. Dual effects of METH on glucose uptake were paralleled to changes in the expression of astrocytic glucose transporter protein-1 (GLUT1). The adaptive nature of astrocyte to mitochondrial β-oxidation of fatty acid appeared to contribute the survival of astrocytes during METH-induced glucose deprivation. This differential adaptive nature of neurons and astrocytes also governed the differential sensitivity to the toxicity of METH in these brain cells. The effect of acetyl-L-carnitine for enhanced production of ATP from fatty oxidation in glucose-free culture condition validated the adaptive nature of neurons and astrocytes. These findings suggest that deprivation of glucose-derived energy may contribute to neurotoxicity of METH abusers

PRIOR ONTOLOGY SELECTION AND QUERY TRANSLATION FOR INFORMATION SEARCH

Objective: Most of the current search engines follow informal keyword based search. Finding the user intention and improving the relevancy of results are the major issues faced by the current traditional keyword based search. Targeting to solve the problems of traditional search and to boost the retrieval process, a framework for semantic based information retrieval is planned. Methods: Social and wine ontologies are used to find the user intention and retrieving it. User's natural language queries are translated into SPARQL (SPARQL Protocol and Resource Description Framework query language) query for finding related items from those ontologies.Results: The proposed method makes a significant improvement over traditional search in terms of some searches required for searching a particular number of pages using performance graph.Conclusion: Semantic based search can understand the user intention and gives better results than traditional search

DYNAMIC LOCATION AREA PLANNING IN CELLULAR NETWORK USING FREQUENT PATTERN MINING

Frequent pattern mining algorithms as the name says, mines sets frequent patterns form given datasets. These algorithms provide immensely helpful results which have a wide scope of application starting from simple decision problems to complex business intelligence aspects. This paper attempts to apply the same concept of frequent pattern mining to solve the location management problem of GSM networks. In GSM networks the task of keeping track of a mobile user (MU) and relaying an incoming call, is called location management. It basically includes two processes, location update and paging. Location update deals with managing the current location of the MUs. There are many approaches to do this like time based, movement based, distance based etc. In this paper the location update procedure relies on the collected data of user movements form one network cell to another cell. This data has a definite pattern, as in day to day life a person mostly has a fixed route of travelling e.g. home to office in the morning and office to home in the evening. During this movement he crosses a specific set of network cells which remains same throughout the week. Thus, frequent pattern mining algorithm can be applied on the user's mobility log and try to find out the most probable location where the mobile user could be found. Using the results, a dynamic location area for individual user's current location can be created. Thus minimizing cost related to location update which otherwise involves communication between the mobile handset and the base station, and calculations related to keeping track of the location of mobile users.Â

Exceptions and Limitations to Intellectual Property Rights with Special Reference to Patent and Copyright Law

This thesis entitled Exceptions and limitations to intellectual property rights with special reference to patent and copyright law.The study on the limitations and exceptions to copyright and patent was mainly characterized by its diversity and flexibility. The unique feature of limited monopoly appended to intellectual property was always a matter of wide controversy.The historical analysis substantiated this instrumentalist philosophy of intellectual property.the study from a legal space characterized by diversity and flexibility and end up in that legal space being characterized by homogeneity and standardization. The issue of flexibility and restrictiveness in the context of TRIPS is the next challenging task. Before devising flexibility to TST, the question to be answered is whether such a mechanism is desirable in the context of TRIPS.In conclusion it is submitted to reorient the intellectual property framework in the context of the noble public interest objectives.Cochin University of Science and Technology.School of Legal Studies, Cochin University of Science and Technology

EECHS-ARO: Energy-efficient cluster head selection mechanism for livestock industry using artificial rabbits optimization and wireless sensor networks

In the livestock industry, wireless sensor networks (WSNs) play a significant role in monitoring many fauna health statuses and behaviors. Energy preservation in WSNs is considered one of the critical, complicated tasks since the sensors are coupled to constrained resources. Therefore, the clustering approach has proved its efficacy in preserving energy in WSNs. In recent studies, various clustering approaches have been introduced that use optimization techniques to improve the network lifespan by decreasing energy depletion. Yet, they take longer to converge and choose the optimal cluster heads in the network. In addition, the energy is exhausted quickly in the network. This paper introduces a novel optimization technique, i.e., an artificial rabbits optimization algorithm-based energy efficient cluster formation (EECHS-ARO) approach in a WSN, to extend the network lifetime by minimizing the energy consumption rate. The EECHS-ARO technique balances the search process in terms of enriched exploration and exploitation while selecting the optimal cluster heads. The experimentation was carried out on a MATLAB 2021a platform with varying sensor nodes. The obtained results of EECHS-ARO are contrasted with other existing approaches via teaching–learning based optimization algorithm (TLBO), ant lion optimizer (ALO) and quasi oppositional butterfly optimization algorithm (QOBOA). The proposed EECHS-ARO enriches the network lifespan by ~15% and improves the packet delivery ratio by ~5%