Method for most appropriate tealeaves harvest timing with Near Infrared (NIR) camera images is proposed. In the proposed method, NIR camera images of tealeaves are used for estimation of nitrogen content in tealeaves. The nitrogen content is highly correlated to Theanine (amid acid) content in tealeaves. Theanine rich tealeaves taste good. Therefore, tealeaves quality can be estimated with NIR camera images. Also, leaf area of tealeaves is highly correlated to NIR reflectance of tealeaf surface. Therefore, not only tealeaf quality but also harvest mount can be estimated with NIR camera images. Experimental results shows the proposed method does work for estimation of appropriate tealeaves harvest timing with NIR camera images.

In this research, four ANN models: Function Fitting (FitNet), Nonlinear AutoRegressive (NARX), Recurrent (RNNs), and Cascaded-ForwardNet were constructed and trained separately to become a filter to remove noise from any speech signal. Each model consists of input, hidden and output layers. Two neurons in the input layer that represent speech signal and its associated noise. The output layer includes one neuron that represent the enhanced signal after removing noise. The four models were trained separately on stereo (noisy and clean) audio signals to produce the clean signal. Experiments were conducted for each model separately with different: architecture; optimization training algorithms; and learning parameters to identify model with best results of removing noise from speech signal. From experiments, best results were obtained from FitNet and NARAX models respectively. TrainLM is the best training algorithm in this case. Finally, the results showed that the suggested architecture of the four models have filtering ability to remove noise form both trained and not trained speech signals samples.

Gender recognition from facial images has become an empirical aspect in present world. It is one of the main problems of computer vision and researches have been conducting on it. Though several techniques have been proposed, most of the techniques focused on facial images in controlled situation. But the problem arises when the classification is performed in uncontrolled conditions like high rate of noise, lack of illumination, etc. To overcome these problems, we propose a new gender recognition framework which first preprocess and enhances the input images using Adaptive Gama Correction with Weighting Distribution. We used Labeled Faces in the Wild (LFW) database for our experimental purpose which contains real life images of uncontrolled condition. For measuring the performance of our proposed method, we have used confusion matrix, precision, recall, F-measure, True Positive Rate (TPR), and False Positive Rate (FPR). In every case, our proposed framework performs superior over other existing state-of-the-art techniques.

Given a weighted complete digraph, the Linear Ordering Problem (LOP) consists of finding and acyclic tournament with maximum weight. It is sometimes referred to as triangulation problem or permutation problem depending on the context of its application. This study introduces an algorithm for LOP and applied for triangulation of Tanzanian Input-Output tables. The algorithm development process uses Great Deluge heuristic method. It is implemented using C++ programming language and tested on a personal computer with 2.40GHZ speed processor. The algorithm has been able to triangulate the Tanzanian input-output tables of size 79×79 within a reasonable time (1.17 seconds). It has been able to order the corresponding economic sectors in the linear order, with upper triangle weight increased from 585,481 to 839,842 giving the degree of linearity of 94.3%.

Risk identification and assessment in today’s sce-nario play a vital role in any software/web application devel-opment industry. Many process models deliver the process related to development life cycle, but the risk assessment at an early stage is still an issue and is a significant subject for research. In this paper, an approach based on MVC architecture by embedding spiral process, which is verified and validated by V-shape model is proposed. By using this approach development efficiency will increase due to less burdened working team(s), reduces stressful maintenance effort that causes reduction in risk factors because of beautifully distributed human effort to improve software quality. Besides, the efficiency of our approach is manifested by the preliminary experiment.

Semantic similarity calculation models are found in many applications, with the aim to give additional knowledge to reason about their data. The choice of a similarity measure is quite crucial for a successful implementation of reasoning. In this work, we present an update of similarity calculation presented by Wu and Palmer which is considered the fastest in time generation of similarity. The results obtained show that the measure produced provides a significant improvement in the relevance of the values produced for the similarity of two concepts in ontology.

Several members are involved in development and management of the Distributed Software Projects. Each member needs to know the responsibilities of each other for proper management of the activities of such distributed projects to produce coherent outcomes. Distribution middleware software has higher-level distributed programming models whose reusable APIs (application programming interface) and components automate and extend native operating system capabilities. Software management tools like Work break-down structure (WBS), Gantt chart, Critical Path Method, and Critical Chain Method etc. does not fully help the managers to manage the member's responsibilities during the development of distributed applications. The layered architecture can help to do so. This style not only gives the layer level description of the activity involved, it also defines and directs the group of workforce. By listing the groups of workforce, the development team as well as the customer can know the activity and the member involved to work on those specific activities. This layered architecture is much benefited to development team and also to numbers of stakeholder of the large distributed project. The extended new approach of layer pattern with 'Responsibility Index' adds extra value to manage all the members' responsibilities. Managers, stakeholders and others can have an easy management system. The request or complaint from the customer can be passed to appropriate team without much delay. Most importantly this will give facility to collect timely feedback from all levels of customers.

There is a need to a small set of words –known as a query– to searching for information. Despite the existence gap between a user’s information need and the way in which such need is represented. Information retrieval system should be able to analyze a given query and present the appropriate web resources that best meet the user’s needs. In order to improve the quality of web search results, while increasing the user’s satisfaction, this paper presents the current work to identify user’s intent sources and how to understand the user behavior and how to discover the users’ intentions during the web search. This paper also discusses the social network analysis and the web queries analysis. The objective of this paper is to present the challenges and new research trends in understanding the user behavior and discovering the user intent to improve the quality of search engine results and to search the web quickly and thoroughly.

Mining sequential patterns from sequence database has consequential responsibility in the data mining region as it can find the association from the ordered list of events. Mining methods that predicated on the pattern growth approach, such as PrefixSpan, are well-organized enough to denude the sequential patterns, but engendering a projection database for each pattern regards as bottleneck of these methods. Lin (2008) first commenced the concept of tree structure to sequential pattern mining, which is acknowledged as Fast updated sequential pattern tree (FUSP - tree). However, link information stored in each node of FUSP - tree structure increases the complication of this method due to its link updating process. In this paper, at first, we have proposed a modified fast updated sequential pattern tree (called a mFUSP - tree) arrangement for storing the complete set of sequences with just frequent items, their frequencies and their relations among items in the given sequence into a compact data structure; excluding this tree structure avoids storing link information along to the next node of the following branch in the tree that carries the same item. Afterward, we have established by a mining method that our mFUSP - tree structure is proficient enough to ascertain out the perfect set of frequent sequential patterns from sequence databases without generating any intermediate projected tree and without calling for repeated scanning of the original database during mining. Our experimental result proves that, the performance of our proposed mFUSP - tree mining approach is a lot more trustworthy than other existing algorithms like GSP, PrefixSpan and FUSP - tree based mining.

Software reliability models assess the reliability by predicting faults for the software. Reliability is a real world phenomenon with many associated real-time problems. To obtain solutions to problems quickly, accurately and acceptably, a large number of soft computing techniques have been developed, but it is very difficult to find out which one is the most suitable and can be used globally. In this paper, we have provided an overview of existing soft computing techniques, and then critically analyzed the work done by the various researchers in the field of software reliability. Further to this, we have also compared soft computing techniques in terms of software reliability modeling capabilities.