A New Clustering Algorithm for Comparable Entities from Web

In internet comparison activity performed by users for decision making .It is very difficult what to compare and what are alternatives. The comparable entities can be used to help users make alternate decisions by comparing relevant mining entities. Several approaches exist to extract comparable entities from various web corpuses. Existing entity mining techniques focus on mining comparable pairs readily observed in the web corpus. a weakly-supervised bootstrapping method can be used to identify comparative questions, comparative patterns, and extract comparable entities. But our work focuses on predicting pairs that cannot be observed from it. For this we develop TricluQueue clustering approach for comparative question identification and comparable entities extraction. We aim to find clusters in which all entities within the same cluster are comparable to each other

Structure-function-folding relationships and native energy landscape of dynein light chain protein: nuclear magnetic resonance insights

The detailed characterization of the structure, dynamics and folding process of a protein is crucial for understanding the biological functions it performs. Modern biophysical and nuclear magnetic resonance (NMR) techniques have provided a way to obtain accurate structural and thermodynamic information on various species populated on the energy landscape of a given protein. In this context, we review here the structure-function-folding relationship of an important protein, namely, dynein light chain protein (DLC8). DLC8, the smallest subunit of the dynein motor complex, acts as a cargo adaptor. The protein exists as a dimer under physiological conditions and dissociates into a pure monomer below pH 4. Cargo binding occurs at the dimer interface. Dimer stability and relay of perturbations through the dimer interface are anticipated to be playing crucial roles in the variety of functions the protein performs. NMR investigations have provided great insights into these aspects of DLC8 in recent years

Detection of marine aerosols with IRS P4-ocean colour monitor

The atmospheric correction bands 7 and 8 (765nm and 865nm respectively) of the Indian Remote Sensing Satellite IRS P4-0CM (Ocean Colour Monitor) can be used for deriving aerosol optical depth (AOD) over the oceans. A retrieval algorithm has been developed which computes the AOD using band 7 data by treating the ocean surface as a dark background after removing the Rayleigh path radiance in the sensor-detected radiances. This algorithm has been used to detect marine aerosol distributions at different coastal and offshore locations around India. A comparison between OCM derived AOD and the NOAA operational AOD shows a correlation ~0.92 while that between OCM derived AOD and the ground-based sun photometer measurements near the coast of Trivandrum shows a correlation of ~0.90

IMPACT OF EFFICIENT CASH MANAGEMENT IN THE FINANCIAL PERFORMANCE OF SMALL AND MEDIUM ENTERPRISES (SMEs)

This research paper examines the impact of efficient cash management on the performance of Small and Medium Enterprises (SMEs) in the north coastal districts of Andhra Pradesh. The study involves both primary and secondary data analysis and seeks to elucidate the direct correlation between cash management techniques and SMEsperformance. Small and Medium Enterprises (SMEs) play an important role in any economy as they can generate employment, promoting the growth of Gross Domestic Product (GDP), embarking on innovations, and stimulating of other economic activities. The SME sector is said to be the backbone of all developed and developing nations. The development of the SME sector is of paramount importance for any country irrespective of their level of development since this sector has immense potential to generate maximum socio-economic benefits to the country with the minimum level of investment. The available statistics indicate that a vast majority of the small and medium scale industries die within their first five years of their existence and a few firms go into between the six to ten years of existence. It may be noted that only 5 to10 percent of the small and medium enterprises can survive, thrive, and grow into maturity stage. The reason for this is not an only capital shortage but may be improper cash management practices. In this paper, an attempt is made to analysethe cash management practices in small and medium enterprises. It is a descriptive study with a structured questionnaire from a sample of 360 which includes 346 small and 14 medium scale enterprises in Visakhapatnam, Srikakulam and Vizianagaram district

Financial Risk Assessment using Machine Learning Engineering (FRAME): Scenario based Quantitative Analysis under Uncertainty

Risk management functions, under uncertainty, in the Banking Industry have been changing and will continue to change with the recent advancements and innovations. Embracing uncertainty and working with measurable risk becomes critical, therefore quantitative risk severity assessment is critical for sustainable financial excellence. In this paper, the authors propose Financial Risk Assessment using Machine Learning Engineering (FRAME)  based on artificial intelligence (AI) and machine learning (ML), which has two significant contributions. Firstly, adoption of machine learning models for banking towards risk quantification and secondly, granularity that emphases on customized logic via multi-factor analysis modeling at different levels of abstraction connecting machine learning models. These contributions will help Financial Institutions (Fis) that will gain the most benefits and opportunities.  In a nutshell, the framework analysis presented in this paper is intended as a step towards building a framework of risk modeling from qualitative to quantitative, viewed at different levels of abstraction to access risk severity in the banking applications

Labelled Classifier with Weighted Drift Trigger Model using Machine Learning for Streaming Data Analysis

The term “data-drift” refers to a difference between the data used to test and validate a model and the data used to deploy it in production. It is possible for data to drift for a variety of reasons. The track of time is an important consideration. Data mining procedures such as classification, clustering, and data stream mining are critical to information extraction and knowledge discovery because of the possibility for significant data type and dimensionality changes over time. The amount of research on mining and analyzing real-time streaming data has risen dramatically in the recent decade. As the name suggests, it’s a stream of data that originates from a number of sources. Analyzing information assets has taken on increased significance in the quest for real-time analytics fulfilment. Traditional mining methods are no longer effective since data is acting in a different way. Aside from storage and temporal constraints, data streams provide additional challenges because just a single pass of the data is required. The dynamic nature of data streams makes it difficult to run any mining method, such as classification, clustering, or indexing, in a single iteration of data. This research identifies concept drift in streaming data classification. For data classification techniques, a Labelled Classifier with Weighted Drift Trigger Model (LCWDTM) is proposed that provides categorization and the capacity to tackle concept drift difficulties. The proposed classifier efficiency is contrasted with the existing classifiers and the results represent that the proposed model in data drift detection is accurate and efficient

A REALISTIC AND VALUABLE VARIETY SELECTION POLICY FOR HUGE RANGE DE-DUPLICATION

The data provided using the customer to tune the deduplication process is generally symbolized having a couple of by hands labeled pairs. In large datasets, producing this type of labeled set may well be a daunting task because it requires a specialist to choose and label plenty of informative pairs. Within the first stage, we advise a procedure for produce balanced subsets of candidate pairs for labeling. Within the second stage, an active selection is incrementally invoked to get rid of the redundant pairs within the subsets produced within the first stage to be able to provide an even smaller sized plus much more informative training set. This training set is effectively used both to understand in which the most ambiguous pairs lie also to configure the classification approaches. Our evaluation makes sure that TSSS cuts lower around the labeling effort substantially while achieving a hostile or superior matching quality in comparison with condition-of-the-art deduplication methods in large datasets. The information deduplication task has attracted lots of attention inside the research community to be able to provide efficient and effective solutions

Underwater spray and wait routing technique for mobile ad-hoc networks

1648-1655The underwater mobile ad-hoc networks comprise sensor nodes that are source nodes for gathering underwater-related data. Relay nodes are the mobile nodes for collecting data from sensor nodes and achieving intermittent connectivity among source and destination nodes. Developing an efficient routing protocol for underwater communication is a challenging issue due to limitations of the underwater environment. Underwater mobile ad-hoc networks are intermittent networks where end-to-end path does not exist from source to destination. To overcome these problems a delay and disruption tolerant network (DTN) is a good solution. In the current paper, we consider the Spray and Wait (SaW) routing technique. In SaW, source and relay nodes represents the moving nodes, and they try to send data to destination nodes. Based on this, we propose the replica based underwater SaW (USaW) routing for underwater mobile ad-hoc networks. In USaW, source nodes are fixed to the bottom of the surface. Underwater sensor nodes replicate sensor data and provide maximum copies of data to the relay nodes that they encounter. In generally, relay nodes have high capability of transmitting data as compared to sensor nodes in an underwater environment. We analyze the performance of USaW with respect to delivery ratio, network throughput, energy consumption, end-to-end delay, and packet drop rate comparing with existing SaW and prophet routing protocols