Automatic Discovery and Ranking of Synonyms for Search Keywords in the Web

Search engines are an indispensable part of a web user's life. A vast majority of these web users experience difficulties caused by the keyword-based search engines such as inaccurate results for queries and irrelevant URLs even though the given keyword is present in them. Also, relevant URLs may be lost as they may have the synonym of the keyword and not the original one. This condition is known as the polysemy problem. To alleviate these problems, we propose an algorithm called automatic discovery and ranking of synonyms for search keywords in the web (ADRS). The proposed method generates a list of candidate synonyms for individual keywords by employing the relevance factor of the URLs associated with the synonyms. Then, ranking of these candidate synonyms is done using co-occurrence frequencies and various page count-based measures. One of the major advantages of our algorithm is that it is highly scalable which makes it applicable to online data on the dynamic, domain-independent and unstructured World Wide Web. The experimental results show that the best results are obtained using the proposed algorithm with WebJaccard

Chemical Constituents, Antioxidant, and Antimicrobial Activity of Allium chinense G. Don

Background Allium chinense G. Don is a medicinal and aromatic herb belonging to the family Amaryllidaceae, characterized by a high saponin content. The previous report has mostly been focused on the bulb, and there is very limited work on the leaf. The information about biological and chemical constituent of A. chinense leaf is still inadequate in contrast to the investigations reported on the bulb. To the best of our knowledge, there is no report on the hexane extract of both bulb and leaf extract. Therefore, the present investigation was focused on identifying and characterization of the hexane extracts of A. chinense bulb and leaf quantitatively and by using the GC-MS method and to know its scavenging, antibacterial, and antifungal activity. Results Twenty-eight bioactive compounds were identified in the bulb and nine in the leaf extract by GC-MS analysis. The versatile compounds present in the bulb are 2-methyloctacosane (21.30%), tetracontane (14.05%), eicosane, 10-methyl (12.06%), heneicosane (8.46%), octadecyl trifluoroacetate (6.48%), and 1-heneicosanol (5.76%), whereas in the leaf, it was phytol (35.76%), tetratetracontane (18.49%), perhydrofarnesyl acetone (14.76%), and heptadecane, 2,6-dimethyl (10.79%). In quantitative estimation, saponins were estimated to have the highest with 375.000 ± 0.577 mg/g in the leaf and 163.750 ± 0.433 mg/g in the bulb. The DPPH antioxidant scavenging activity was found to be minimum in both the bulb (IC50 = 678.347 μg/ml) and leaf (IC50 = 533.337 μg/ml). A. chinense extracts of both leaf and bulb exerted potential antibacterial effects against Staphylococcus aureus and Pseudomonas aeruginosa. Leaf extract exhibited greater antifungal activity than the bulb against Aspergillus niger. Conclusion From the analysis, the hexane leaf extract exhibited higher antibacterial, antifungal, and antioxidant activity than the bulb. Their superior activity might be due to the higher content of total saponin and terpenes. This result will lead to further in-depth research towards the potential use of this plant; the bio-constituents can be further isolated and used in medical and therapeutic applications

Dynamic management of traffic signals through social IoT

Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed

AN EPIDEMIOLOGICAL SURVEY ON AWARENESS AND PSYCHOLOGICAL CONCERN OF MALOCCLUSION AMONG 10-16 YEARS SCHOOL CHILDREN IN KARNATAKA

Aims: To assess the awareness and psychological concern of malocclusion among 10-16 years school children in Karnataka State.Settings and Design: School settings and Descriptive cross-sectional survey.Methods and Material: A cross-sectional epidemiological survey was conducted in all the 30 districts of Karnataka. School children in the age group of 10-16 years were the target population. Population proportionate technique was employed for the sample size estimation. Two sets of pre-structured questionnaire were constructed and used as study tool.Statistical analysis used: Simple Descriptive statistics.Results and Conclusion: According to the results of the study a good awareness in children regarding orthodontic treatment and psychological concern about their personal appearance and social issues is observed

TASB-AC: Term Annotated Sliding-Window-Based Boosting Associative Classifier for DNA Repair Gene Categorization

Damage to DNA affects the biochemical pathways of the cell and leads to aging, if not repaired. Several genes in the genome of an organism are responsible for DNA repair activities, however, not all of them are related to the biological aging process. In this paper, we develop a data mining technique to relate association of DNA repair genes with the aging process of the organism. Nucleotide sequence of the DNA repair genes is annotated with their respective biochemical properties and is then converted to a transactional dataset. Further, biological features are extracted from the dataset by constructing an associative classifier. To select significant gene features, we employ sliding-window technique to divide the gene sequence into subsequences and thus increase their count. An extensive evaluation is performed of the proposed technique by taking human DNA repair genes along with their biochemical

Drcm: Dynamic Relationship Creation and Management in Social Internet of Things

As internet of things (IoT) is overpopulated with a multitude of objects, services and interactions locating the most relevant object is emerging as a major obstacle. Over the last few years, the social internet of things (SIoT) paradigm, where objects independently establish social relationships with the other things has become popular as it provides several new characteristics to carryout reliable discovery approaches. Given a large scale deployment of socially connected objects, finding the shortest path to reach the service provider remains as a fundamental challenge. Most of the existing techniques, search for a specific object or service utilising its friendship or friends of friends connections. As a result, each object has to manage a large set of friends, thus slowing down the search process. In this paper, we propose a similarity based object search mechanism that dynamically creates and manages relationships based on physical location proximity and social context of users in social communities. The result shows enhancement in the proposed method over the existing search techniques FSS, FSASV and LSFGA in terms of local cluster coefficients, the average degree of connections and average path length

Energy aware resource allocation and complexity reduction approach for cognitive radio networks using game theory

Nowadays, the demand for mobile wireless communication systems has increased drastically due to its significant use for various real-time applications. This increased demand for communication causes heavy utilization of the radio spectrum to improve ubiquitous computing services. However, systems providing high-speed communication fail to achieve the desired performance due to unsystematic spectrum utilization and resources. The problem addressed by Cognitive Radio Network (CRN) architecture has attracted research and industrial community to enhance the real-time communication systems. Although CRN based real-time communication systems suffer from resource allocation, spectrum sensing, and power consumption issues. In this paper, we introduce a novel approach for resource allocation and sharing based on cooperative game theory, and cooperative node selection ensures maximized

SSSSS: search for social similar smart objects in SIoT

As Internet of Things (IoT) is overpopulated with multitude of objects, services and interactions, efficiently locating the most relevant object is emerging as a major obstacle. Over the last few years, the Social Internet of Things (SIoT) paradigm, where objects independently establish social relationships among them, has become more popular as it provides a number of exciting characteristics to boost network navigability and carryout reliable discovery approaches. Given a large scale deployment of socially connected objects, finding the shortest path to reach the service provider remains as a fundamental challenge. In most of the existing search techniques, the physical significance of the objects is not very well explained and the geographical location of mobile objects is not considered. In this paper, to improve the search performance over the SIoT, we propose a novel object search mechanism based on physical

DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic