Miracle-dz/UniFAR-FTUs huggingface dataset:
Anticipation of Software Development Effort using Artificial Neural Network for NASA Data Sets

: Failures of software are mainly due to the faulty project management practices, which include effort estimation. Continuous changing outlines of software development technology make effort estimation more challenging. Several methods are available in order to estimate the effort among which soft computing based method plays a prominent role. Software effort estimation is to find out the development effort required by a project. Before any project is being started initially it is required to estimate the development effort required. This is one of the most important and challenging activities that has done before. Software cost estimation deals with lot of uncertainty among all soft computing methods neural network is good in handling uncertainty. In this project we have used artificial neural network to predict the effort estimation using the software Mat lab -11. The Dataset used for analysis and validation of the model can be got from historic projects of NASA. Various techniques have been proposed to calculate effort estimation, but providing accurate cost is still an unachievable goal.
A Review on Various Clustering Protocols in Vehicular Adhoc Network (VANETs)

The new study in cluster-based MAC and redirecting schemes for Vehicle Offer Hoc Networks (VANETs) motivates the prerequisite for a well balanced VANET clustering algorithm. As a result of highly cellular nature of VANETs, mobility must play an integrated position in group formation. We present a story, mobility-based clustering scheme for Vehicle Offer hoc Networks, which employs the Affinity Propagation algorithm in a spread manner. The proposed algorithm views common vehicular mobility throughout group development, which provides clusters with high stability. Simulation results confirm the superior efficiency of the proposed algorithm, when comparing to different acknowledged mobility-based clustering techniques. Clustering efficiency is assessed when it comes to average group head period, average group member period, average rate of group head change, and average amount of clusters. Cellular communities in compared to wired communities have distinctive characteristics. In cellular communities, regular system topology improvements may bring about by node mobility, which are rare in wired networks. In comparison to the secure url capacity of wired communities, wireless url capacity continually differs because of the affects from transmission energy, phone sensitivity, noise, fading and interference. Furthermore, wireless cellular communities have a high error rate, energy restrictions and bandwidth limitations.
Automated Detection of Acute Myelogenous Leukemia Using Neural Classifier

— “Acute myeloid leukemia (AML)” is a form of cancer. In this case, abnormal myeloblasts (a type of white blood cell), red blood cells, or platelets are get formed within the bone marrow. AML is a quickly developing cancer of the blood and bone marrow. It is deadly if left untreated, because of its quick spread into the circulatory system and other fundamental organs. This is more prevalent among adults with an average age 65 years. The present strategies for AML detection include manual examination of the blood smear as the ﬁrst step. Diagnosing leukemia depends on the way that white cell tally is expanded with immature blast cells (lymphoid or myeloid), and neutrophils and platelets are decreased. Thusly, hematologists routinely look at blood spread under magnifying instrument for legitimate identiﬁcation and classiﬁcation of blast cells. The presence of the abundance number of blast cells in blood is a signiﬁcant sign of leukemia. It is di ﬃ cult to detect leukemia because blood smear images are of complex nature. The imitation of similar signs of other disorders are also a main factor that make leukemia detection difficult. Moreover, the detection process need more time to diagnose and sometimes it is susceptible to errors. Hence, there is a need for automation of leukemia detection. This paper makes a survey that helps in analyzing the methodologies in detecting AML using the algorithms from neural networks. The proposed method is relied upon to deliver better results in accuracy and time consumption. Fault tolerance of neural algorithms are expected to produce more realistic results in very short time as compared with others .
Humming Query Music Search In the Database.

Recently, the necessity for content-based music retrieval that can return results even if a user does not know information such as the title or singer has increased. Query-by-humming (QBH) systems have been introduced to address this need, as they allow the user to simply hum snatches of the tune to find the right song. Query by humming (QBH) means to search a piece of music by singing or humming. Given melodies hummed by the users, query by humming systems will return an ordered list of songs according to the similarity between humming’s and target songs. Query by Singing/Humming (QBSH) is a Music Information Retrieval (MIR) system with small audio excerpt as query. For our work, the input is humming sound which is sound wave and Musical Instrument Digital Interface (MIDI) is used as the reference song in database. In this paper, a system for querying an audio database by humming is described along with a scheme for representing the melodic information in a song as relative pitch changes. We are using Dynamic Time Wrapping Algorithm (DTW) for searching.
DC Voltage Controller for Asymmetric Twin Conversion STATCOM for High Power Applications

The proposed controller can balance individual dc capacitor voltages when H-bridges run with different switching patterns and have parameter variations. It has two advantages: 1) the controller can work well in all operation modes (the capacitive mode, the inductive mode, and the standby mode) and 2) the impact of the individual dc voltage controller on the voltage quality is small. This paper suggests further improvements. A three-phase cascaded H-bridge inverter, this control method uses a discrete-time model of the system to predict the future value of the current for all voltage vectors, and selects the vector which minimizes a cost function. Analytical solutions of pulse width-modulation (PWM) strategies for multilevel inverters are used to identify that alternative phase opposition disposition PWM. In order to improve the performance, a phase-shifted carrier based pulse width modulation technique is used. A mathematical model of the system is derived, based on which a controller for the scheme is designed. The effectiveness of this scheme is verified through detailed simulation study.
Opportunistic Computing Framework for Mobile-Healthcare Emergency

-- With the pervasiveness of smart phones and the advance of wireless body sensor networks (BSNs), mobile Healthcare (m-Healthcare), which extends the operation of Healthcare provider into a pervasive environment for better health monitoring, has attracted considerable interest recently. However, the flourish of m-Healthcare still faces many challenges including information security and privacy preservation. In this paper, we propose a secure and privacy-preserving opportunistic computing framework, called SPOC, for m-Healthcare emergency. With SPOC, smart phone resources including computing power and energy can be opportunistically gathered to process the computing-intensive personal health information (PHI) during m-Healthcare emergency with minimal privacy disclosure. In specific, to leverage the PHI privacy disclosure and the high reliability of PHI process and transmission in m-Healthcare emergency, we introduce an efficient user-centric privacy access control in SPOC framework, which is based on an attribute-based access control and a new privacy-preserving scalar product computation (PPSPC) technique, and allows a medical user to decide who can participate in the opportunistic computing to assist in processing his overwhelming PHI data. Detailed security analysis shows that the proposed SPOC framework can efficiently achieve user-centric privacy access control in m-Healthcare emergency. In addition, performance evaluations via extensive simulations demonstrate the SPOC’s effectiveness in term of providing high-reliable-PHI process and transmission while minimizing the privacy disclosure during m-Healthcare emergency.
Adaptive Boost Learning Approach: An Improved Method for Software Defect Prediction

: An important goal during the cycle of software development is to find and fix existing defects as early as possible. Although, big software development companies have their own development repository, which typically includes a version control system and a bug tracking system. There is no doubt that these systems proved useful for software defects prediction. Still, lot of work has to be done for software defect prediction and management. In this report, two models has been proposed for defect prediction (i) Support Vector Machine (SVM) is combined with Principle Component Analysis (PCA) and (ii) Adaptive Boost of Support Vector Machine(SVM)- Radial Basis Function (RBF) is combined with Principle Component Analysis. Principle Component Analysis has been used for reducing data dimensionality. First, the defect prediction capability of different existing methods without and with Principle Component Analysis has been analyzed using WEKA and MATLAB. The comparative analysis of existing and proposed models demonstrate that the defect prediction capability of Adaptive Boost with SVM-RBF kernel approach is better.
Effect of Parental Modernity on Rejection Sensitivity and Self-Esteem of Adolescents

Parental Modernity is an important aspect for the psycho-social development of the child. The present study aims to study the effect of parental modernity on rejection sensitivity and selfesteem of adolescents and the relationship between rejection sensitivity and self-esteem. The research is carried out on a sample of 240 parents (including 120 fathers and 120 mothers) and their 120 children. For observing the impact of modernity of parents on their children, Individual Modernity Scale was used and administered on father and mother. Rejection Sensitivity Questionnaire and Self-Esteem Inventory were used to measure the rejection sensitivity and selfesteem of children (age ranges from 14 to 19 years). The results suggest that parental modernity has an effect on the rejection sensitivity and personally perceived self of the self – esteem of adolescents. Furthermore, the rejection sensitivity has been found negatively associated with self-esteem.
Just In Time Retrieval System for Document Recommendation

- This paper addresses a diverse retrieval technique for ranking documents that are spontaneously retrieved and recommended to people during a conversation. These documents represent potentially useful information for the conversation participants [1] . In computer forensic analysis, hundreds of thousands of files are usually examined. Much of the data in those files consists of unstructured text, whose analysis by computer examiners is difficult to be performed. In this context, automated methods of analysis are of great interest [2] . A just-in-time information retrieval agent (JITIR agent) is software that proactively retrieves and presents information based on a person’s local context in an easily accessible yet nonintrusive manner [4] . The proposed evaluation method is shown to be reliable, and the results show that adding user initiative improves the relevance of recommendations [5] .
Decolonising the academy: The process of re-presenting indigenous health in tertiary teaching and learning

Abstract Indigenous health workforce development has been identified as a key strategy to improve Indigenous health and reduce ethnic inequities in health outcomes. Likewise, development of a culturally safe and culturally competent non-Indigenous health workforce must also occur if the elimination of health inequities is to be fully realised. Tertiary education providers responsible for training health professionals must face the challenge of engaging the Indigenous learner within health sciences, exposing the ‘hidden curriculum’ that undermines professional Indigenous health learning and ensuring tertiary success for Indigenous students within their academy. This chapter summarises recent developments, research and interventions within the Faculty of Medical and Health Sciences at the University of Auckland that aims to address these challenges by re-presenting Indigenous student recruitment, selection and support, re-presenting bridging/foundation education and representing Māori health teaching and learning within the curriculum.
LOCALITY BASED P2P LIVE MEDIA STREAMING USING CLOUD COMPUTING

- In the modern world multimedia streaming is the most popular applications over the internet. Peer-to-peer (P2P) video streaming is a raising technology that lowers the constraints imposed in the stream vital events by the Internet. Moreover, it has been highly globalized, because of this globalization user demands and their needs increases exponentially. But the problem in the P2P streaming system is satisfying the real time constrains like genesis of stream and its actual distribution to the user and also bandwidth allocation for the particular location. With the multichannel P2P system it is difficult to predict the location and allocate channel bandwidth needed by the user. Congestion in the accessible upload bandwidth, both through the media source and innermost the overlay network, may restrict the quality of service (QoS) accomplished by users. In order to satisfy the user demands it needs an effective technology. So the concept of cloud computing plays a vital role in providing solutions for the existing problems in live media streaming. It is an effective technology which provides enormous support for all the application which is related to internet. The main objectives of cloud are cost effective, flexible, and scalable and it provides a huge amount of storage platform for applications. To provide solution for the bandwidth bottleneck in the streaming network. The main contribution of this thesis is enhancing p2p video streaming with cloud computing to provide better quality of service. The overlay used in dissemination process takes the complexity of O (log n) which reduces the start-up delay in streaming. The first proposed algorithm mainly concentrates on improvising the playback continuity and handling the dynamic nature of peer by managing the computational nodes provided by the cloud on demand and prediction based
SURVEY OF CLASSIFICATION RULE MINING TECHNIQUES FOR IDENTIFYING DISEASE CAUSE AND DIAGNOSIS

-Classification is a supervised learning technique. Classification arises frequently from bioinformatics such as disease classifications using high throughput data like microarrays. Classification rule mining classifies data in constructing a model based on the training set and the values or class labels in a classifying attribute and uses it in classifying new data. Currently, a various modeling techniques are detailed for data mining. The details of data mining and machine knowledge in related and network domains are dependent and comparatively distributed. The technique particularly achieves the statistical belief among occurrences in order to enhance classification accuracy. An attention on dependencies is made where the ability to draw classification accuracy is affected in improving performance of the model. Data partitioning approaches such as bagging and boosting are greatly handled in multiple classifier systems to improve classification accuracy. Most current data stream classification techniques fails in one essential aspect of stream data i.e. arrival of a class. So, a data stream classification method that merges a class detection system into traditional classifiers is enabled. The automatic detection of classes before true labels arrive is detected. The problem of data stream classification, where the data appear in an abstractly limitless stream and the chance to analyze each record is briefed. The searching of a training set accurately and classifying is difficult while considering a large data set. Even with the classified data set the accuracy of the classification is inefficient with error rates. This paper presents classification based on shared information for diagnosing disease. The medical dataset is analyzed with stroke disease reducing error rates providing classification accuracy. This paper also reviews certain data mining papers on classification rule for disease diagnosis patterns.
ISSN 2320-5407 International Journal of Advanced Research (2014), Volume 2, Issue 11, 828-837

Abhishek Sahoo Two hundred and eighty unsexed broiler chicks were distributed randomly into seven dietary treatments (D). The diets were D1: basal diet + mineral mixture without Zn; D2: D1 + 15ppm inorganic Zn; D3: D1 + 15ppm organic Zn; D4: D1 + 7.5ppm organic Zn; D5: D1 + 0.3ppm nano Zn; D6: D1 + 0.06ppm nano Zn; D7: D1 + 0.03ppm nano Zn. Zn concentration in tibia bone, liver and blood serum were the highest in the birds of D6 which differed significantly (P<0.05) with the remaining dietary groups. In case of muscles, the Zn level was highest in the birds of D3 which varied significantly (P<0.05) with that of D1 and D7. The antibody titre to SRBC shows significantly (P<0.05) higher titer levels in D3 and D6 when compared to that of D1 and D2. The CBH response was found to be highest in D3 which differed significantly (P<0.05) with that of D1 and D2. The relative weight of spleen in D3 was significantly (P<0.05) higher than all the other groups. The percent weight of bursa was highest in the birds of D6 which varied significantly (P<0.05) with D1, D4 and D5. Significantly higher (P<0.05) relative weight of thymus was observed in the broilers of D3 when compared to that of D1, D4 and D5. Hence, it was found that 15 ppm organic Zn and 0.06 ppm nano Zn when added to the basal diet improved the health status of the broiler birds.
Feature Extraction and Dimensionality Reduction using IPS for Isolated Tamil Words Speech Recognizer

: Automatic Speech Recognition (ASR), is the process of converting a speech waveform into the text quite similar to the information being communicated by the speaker. This paper aims to construct a speech recognition system for Tamil language. Mel Frequency Cepstral Coefficients (MFCC) is a commonly used feature extraction technique for speech recognition which is computed by applying DCT to the mel-scale filter bank output. Instead of DCT, Integrated Phoneme Subspace (IPS) method is used to improve speech recognition. The experimental results show that the recognition accuracy of ASR using IPS in various forms yields higher or similar output comparative to MFCC and the word accuracy of one such form of IPS (IPS-2) is 84.00%.
A Survey of Digital Image Watermarking Based on different Techniques

--Digital watermarking is the process that embeds data called a watermark into a multimedia object such that the watermark can be detected or extracted later to make an assertion about the object. Several software implementations of the proposed algorithms are available, but very few attempts have been made for protecting intellectual property. Embedded watermark will allow identifying the owner of work hardware implementation. This concept is applicable to digital video and audio also. The goal of this implementation is to survey the different techniques for Digital Watermarking.
AIR POLLUTION TOLERANCE INDEX AND ANTIOXIDANT ACTIVITY OF PLANT LEAVES COLLECTED NEAR RAILWAY JUNCTION, KARRUPUR, SALEM, TAMIL NADU, INDIA

Trees and plants keep the place green and it is essential to protect and preserve plants for various reasons. Hence, the present study was carried out to know about the tolerance, sensitivity of the plants located in the experimental site. Method: Aqueous leaf extract was used for the whole study. Biochemical components, secondary metabolites, antioxidant activities were assessed by following standard procedures. Fresh leaves were collected from the study area daily until the completion of the study. The collected leaves were weighed with dust and without dust i.e after cleaning. The difference gives an idea about the pollution load in the particular place. Results: The ascorbic acid content was high with Tecoma stans, all the other plants showed moderate level of ascorbic acid. The total chlorophyll content was higher with Psidium guajava 45.47±0.00mg/g, Tecoma stans 43.30±0.02mg/g, Tamarindus indica 41.14±0.41mg/g. pH observed in the study area was found to be acidic. The relative water content was high with Citrus limon 95.60±2.90%, Lawsonia inermis 93.18±1.72%. The aminoacid content was higher with Manilkara zapota 2.90±0.12 and moderate with rest of the plants studied. The carbohydrate content was high with Azadirachta indica 8.60±4.50. Air pollution tolerance index of the plants studied were found to be sensitive in the selected experimental site. The phenolic and flavonoid content of the selected plants were significantly high except Tamarindus indica. But, plants such as Citrus limon, Tamarindus indica, Manilkara zapota showed less flavonoid content compared to other plants. The World Journal of Pharmaceutical Research SJIF Impact Factor 6.805 Volume 5, Issue 6, 1536-1551. Research Article ISSN 2277– 7105 *Corresponding Author M. Krishnaveni Assistant Professor,
A Framework for Email Clustering and Automatic Answering Method (Formal Approach)

-The mail-box consisting of number of emails in which many attributes or files are existing in a single email and hence they can be considered as a database. It is not easy to find out the important email or message quickly or within a stipulated time. So we are using clustering techniques for grouping similar emails. The attributes of an email like sender email id, receiver email id, subject, message body, sending time, attachments, cc, bcc, etc. are used for clustering the emails. Email classification can be applied to several different applications, including filtering messages based on priority, assigning messages to user-created folders, or identifying SPAM. In this project we are going to add certain extra features like clustering based on the subject of the email, priority of the person who sends the message, message body contents, date and time, size of the message and also add automatic answering feature for a received mail.
Enhancement of E-commerce security using ETSFS algorithm: A review

Security issues are an important topic in e-commerce. How to protect the security of an e-commerce system and data is its core research area. There are many sensitive financial data and asset data in e-commerce databases, such as transaction records, commercial transactions, user account and market scheme and so on. The data are very important to the parties involved in e-commerce, so we must assure their security completely. To beat these issues, various security strategies have developed to ensure the information in databases. For Example Enhanced Transposition, Substitution, Folding & Shifting (ETSFS). Each of them has its particular benefits and faults. ETSFS provides a high degree of privacy for customers by encrypting payment information. This paper provides a secure and efficient encryption method that encrypts only sensitive data without using special hardware and a mirroring technique to recover the lost data. The purpose of recovery technique is to help the user to retrieve information from any mirror server when server lost his data and unable to provide data to the user. This paper proposes the implementation of the ETSFS algorithm in E-commerce site to make the transaction secure and by using the mirroring technique to recover the data to provide high security to E-commerce site.