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Showing papers in "International Journal of Computer Applications in 2016"


Journal ArticleDOI
TL;DR: A survey which covers Opining Mining, Sentiment Analysis, techniques, tools and classification is presented which covers the polarity of extracted public opinions.
Abstract: In this age, in this nation, public sentiment is everything. With it, nothing can fail; against it, nothing can succeed. Whoever molds public sentiment goes deeper than he who enacts statutes, or pronounces judicial decisions (Abraham Lincoln, 1858 ) [1]. It is apparent from President Lincoln's well known quote that legislators understood the force of open assumption quite a while prior. In today world, the Internet is the main source of information. An enormous amount of information and opinion online is scattered and unstructured with no machine to arrange it. Because of demand the public to know opinions about exact product and services, political issues, or social scientists. That’s led us to study of field Opining Mining and Sentiment Analysis. Opining Mining and Sentiment Analysis have recently played a significant role for researchers because analysis of online text is beneficial for the market research political issue, business intelligence, online shopping, and scientific survey from psychological. Sentiment Analysis identifies the polarity of extracted public opinions. This paper presents a survey which covers Opining Mining, Sentiment Analysis, techniques, tools and classification.

442 citations


Journal ArticleDOI
TL;DR: In the present paper author explore different aspects of gesture recognition techniques, which are the next step in the direction of advance human computer interface.
Abstract: With increasing use of computers in our daily lives, lately there has been a rapid increase in the efforts to develop a better human computer interaction interface. The need of easy to use and advance types of human-computer interaction with natural interfaces is more than ever. In the present framework, the UI (User Interface) of a computer allows user to interact with electronic devices with graphical icons and visual indicators, which is still inconvenient and not suitable for working in virtual environments. An interface which allow user to communicate through gestures is the next step in the direction of advance human computer interface. In the present paper author explore different aspects of gesture recognition techniques.

358 citations


Journal ArticleDOI
TL;DR: In this paper, a survey and a comparative analysis of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics are provided, and general challenges and applications of Sentiment Analysis on Twitter are discussed.
Abstract: With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics, have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide research on twitter data streams.We have also discussed general challenges and applications of Sentiment Analysis on Twitter.

235 citations


Journal ArticleDOI
TL;DR: The results show that LSTM based neural networks are competitive with the traditional methods and can be considered a better alternative to forecast general weather conditions.
Abstract: The aim of this paper is to present a deep neural network architecture and use it in time series weather prediction. It uses multi stacked LSTMs to map sequences of weather values of the same length. The final goal is to produce two types of models per city (for 9 cities in Morocco) to forecast 24 and 72 hours worth of weather data (for Temperature, Humidity and Wind Speed). Approximately 15 years (2000-2015) of hourly meteorological data was used to train the model. The results show that LSTM based neural networks are competitive with the traditional methods and can be considered a better alternative to forecast general weather conditions.

175 citations


Journal ArticleDOI
TL;DR: The survey of various clustering techniques, the current similarity measures based on distance based clustering, explains the limitations associated with the existing clustering technique and proposes that the combination of the advantages of the existing systems can help overcome the limitations of theexisting systems.
Abstract: Clustering is an unsupervised learning technique which aims at grouping a set of objects into clusters so that objects in the same clusters should be similar as possible, whereas objects in one cluster should be as dissimilar as possible from objects in other clusters. Cluster analysis aims to group a collection of patterns into clusters based on similarity. A typical clustering technique uses a similarity function for comparing various data items. This paper covers the survey of various clustering techniques, the current similarity measures based on distance based clustering, explains the limitations associated with the existing clustering techniques and propose that the combination of the advantages of the existing systems can help overcome the limitations of the existing systems. General Terms Data Mining, Machine Learning, Clustering, Pattern based Similarity, Negative Data, et. al.

92 citations


Journal ArticleDOI
TL;DR: A brief review of few common path tracking techniques used in the design of autonomous vehicles and proposes an area where feature research can be done such as tracking of both implicit and explicit path for a non-holonomic mobile robot.
Abstract: paper gives a brief review of few common path tracking techniques used in the design of autonomous vehicles. Technique such as pure-pursuit, vector pursuit as well as CF- pursuit which are all based on the pure-pursuit techniques were discussed and a detailed comparism was made between these geometric techniques. Also this review work discusses areas were little research has been done. Areas such as tracking of an implicit part of a mobile robot and proposes an area where feature research can be done such as tracking of both implicit and explicit path for a non-holonomic mobile robot.

89 citations


Journal ArticleDOI
TL;DR: In this study, code clones, common types of clones, phases of clone detection, the state-ofthe-art in code clone detection techniques and tools, and challenges faced byclone detection techniques are discussed.
Abstract: If two fragments of source code are identical or similar to each other, they are called code clones. Code clones introduce difficulties in software maintenance and cause bug propagation. Software clones occur due to several reasons such as code reuse by copying pre-existing fragments, coding style, and repeated computation using duplicated functions with slight changes in variables or data structures used. If a code fragment is edited, it will have to be checked against all related code clones to see if they need to be modified as well. Removal, avoidance or refactoring of cloned code are other important issues in software maintenance. However, several research studies have demonstrated that removal or refactoring of cloned code is sometimes harmful. In this study, code clones, common types of clones, phases of clone detection, the state-ofthe-art in code clone detection techniques and tools, and challenges faced by clone detection techniques are discussed.

86 citations


Journal ArticleDOI
TL;DR: This paper presents a complete literature review on various feature selection methods for high-dimensional data and employs them for supervised learning algorithms and unsupervised learning algorithms.
Abstract: selection is a process of removing the redundant and the irrelevant features from a dataset to improve the performance of the machine learning algorithms. The feature selection is also known as variable selection or attribute selection. The features are also known as variables or attributes. The machine learning algorithms can be roughly classified into two categories one is supervised learning algorithm and another one is unsupervised learning algorithm. The supervised learning algorithms learn the labeled data and construct learning models that are known as classifiers. The classifiers are employed for classification or prediction to identify or predict the class-label of the unlabeled data. The unsupervised learning algorithms lean the unlabeled data and construct the learning models that known as clustering models. The clustering models are employed to cluster or categorize the given data for predicting or identifying their group or cluster. Mostly, the feature selections are employed for the supervised learning algorithms since they suffered by the high-dimensional space. Therefore, this paper presents a complete literature review on various feature selection methods for high-dimensional data.

84 citations


Journal ArticleDOI
TL;DR: This paper presents retrieval of images based on color and texture using various proposed algorithms in Content Based Image Retrieval (CBIR).
Abstract: A Content Based Image Retrieval System is a computer system for browsing, searching and retrieving images from a large database of digital images .Most common methods of image retrieval utilize some method of adding meta data such as captioning, keywords or description to the images so that retrieval can be performed over the annotation words. Content Based Image Retrieval (CBIR) deals with retrieval of images based on visual features such as color, texture and shape. This paper presents retrieval of images based on color and texture using various proposed algorithms.

74 citations


Journal ArticleDOI
TL;DR: Algorithms for sentiment analysis are studied, challenges and applications appear in this field are discussed and various algorithms available for opinion mining are studied.
Abstract: Opinion mining and sentiment analysis is rapidly growing area. There are numerous e-commerce sites available on internet which provides options to users to give feedback about specific product. These feedbacks are very much helpful to both the individuals, who are willing to buy that product and the organizations. An accurate method for predicting sentiments could enable us, to extract opinions from the internet and predict customer‟s preferences. There are various algorithms available for opinion mining. Before applying any algorithm for polarity detection, pre-processing on feedback is carried out. From these pre-processed reviews opinion words and object on which opinion is generated are extracted and any opinion mining technique is applied to find the polarity of the review. Opinion mining has three levels of granularities: Document level, Sentence level and Aspect level. In this paper various algorithms for sentiment analysis are studied and challenges and applications appear in this field are discussed.

66 citations


Journal ArticleDOI
TL;DR: Overall performance is evaluated through experimental tests by creating real time fire hazard prototype scenarios to investigate reliability and it is observed that SFF system demonstrated its efficiency most of the cases perfectly.
Abstract: Safe From Fire (SFF) is an intelligent self controlled smart fire extinguisher system assembled with multiple sensors, actuators and operated by micro-controller unit (MCU). It takes input signals from various sensors placed in different position of the monitored area, and combines integrated fuzzy logic to identify fire breakout locations and severity. Data fusion algorithm facilitates the system to discard deceptive fire situations such as: cigarette smoke, welding etc. During the fire hazard SFF notifies the fire service and others by text messages and telephone calls. Along with ringing fire alarm it announces the fire affected locations and severity. To prevent fire from spreading it breaks electric circuits of the affected area, releases the extinguishing gas pointing to the exact fire locations. This paper presents how this system is built, components, and connection diagram and implementation logic. Overall performance is evaluated through experimental tests by creating real time fire hazard prototype scenarios to investigate reliability. It is observed that SFF system demonstrated its efficiency most of the cases perfectly.

Journal ArticleDOI
TL;DR: This paper describes an approach to the idea of implementing web-based artificially intelligent chat-bot as a personal assistant of the user, which stimulates setting and initiating meetings of user with his clients.
Abstract: Chat-bots are computer programs coded to have a textual or verbal conversation which is logical or intelligent. Chat-bots are designed to make humans believe that they are talking to a human; but instead they are in fact talking to a machine. Taking advantage of this transparency property of chat-bot, an artificial character and personality can be given to a chat-bot which acts like a person of a specific profession. This paper describes an approach to the idea of implementing web-based artificially intelligent chat-bot as a personal assistant of the user, which stimulates setting and initiating meetings of user with his clients. The exchange of information happens through email conversations whereas its evaluation happens through natural language procession and natural language generation and AIML files. General Terms AIML, Artificial Intelligence, Pattern Matching.

Journal ArticleDOI
TL;DR: The generally used techniques for Heart Disease Prediction and their complexities are summarized in this paper and it is observed that Fuzzy Intelligent Techniques increase the accuracy of the heart disease prediction system.
Abstract: The Healthcare trade usually clinical diagnosis is ended typically by doctor’s knowledge and practice. Computer Aided Decision Support System plays a major task in medical field. Data mining provides the methodology and technology to alter these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. Among the increasing research on heart disease predicting system, it has happened to significant to categories the research outcomes and gives readers with an outline of the existing heart disease prediction techniques in each category. Data mining tools can answer trade questions that conventionally in use much time overriding to decide. In this paper we study different papers in which one or more algorithms of data mining used for the prediction of heart disease. As of the study it is observed that Fuzzy Intelligent Techniques increase the accuracy of the heart disease prediction system. The generally used techniques for Heart Disease Prediction and their complexities are summarized in this paper.

Journal ArticleDOI
TL;DR: The main goal of this work is to develop an image processing system that can identify and classify the various paddy plant diseases affecting the cultivation of paddy namely brown spot disease, leaf blast disease and bacterial blight disease.
Abstract: In agricultural field, paddy cultivation plays a vital role. But their growths are affected by various diseases. There will be decrease in the production, if the diseases are not identified at an early stage. The main goal of this work is to develop an image processing system that can identify and classify the various paddy plant diseases affecting the cultivation of paddy namely brown spot disease, leaf blast disease and bacterial blight disease. This work can be divided into two parts namely, paddy plant disease detection and recognition of paddy plant diseases. In disease detection, the disease affected portion of the paddy plant is first identified using Haar-like features and AdaBoost classifier. The detection accuracy rate is found to be 83.33%. In disease recognition, the paddy plant disease type is recognized using Scale Invariant Feature Transform (SIFT) feature and classifiers namely k-Nearest Neighbour (k-NN) and Support Vector Machine (SVM). By this approach one can detect the disease at an early stage and thus can take necessary steps in time to minimize the loss of production. The disease recognition accuracy rate is 91.10% using SVM and 93.33% using k-NN.

Journal ArticleDOI
TL;DR: A simple approach is used to design stop-word removal algorithm and its implementation for Sanskrit language and the algorithm and the implementation uses dictionary based approach.
Abstract: In the Information era, optimization of processes for Information Retrieval, Text Summarization, Text and Data Analytic systems becomes utmost important. Therefore in order to achieve accuracy, extraction of redundant words with low or no semantic meaning must be filtered out. Such words are known as stopwords. Stopwords list has been developed for languages like English, Chinese, Arabic, Hindi, etc. Stopword list is also available for Sanskrit language. Stop-word removal is an important preprocessing techniques used in Natural Language processing applications so as to improve the performance of the Information Retrieval System, Text Analytics & Processing System, Text Summarization, Question-Answering system, stemming etc. In this paper, a simple approach is used to design stop-word removal algorithm and its implementation for Sanskrit language. The algorithm and its implementation uses dictionary based approach. In dictionary based approach predefined list of stopwords is compared to the target text on which removal is required.

Journal ArticleDOI
TL;DR: Investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done, and experiments show that a weighted naive bayes approach outperforms naive Bayes.
Abstract: this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Bayes, and to improve its performance, weighted concept is incorporated. Exploration of Domain knowledge based weight assignment on UCI machine learning repository dataset of breast cancer is performed. As Breast cancer is considered to be second leading cause of death in women today. The experiments show that a weighted naive bayes approach outperforms naive bayes. KeywordsMining, Breast cancer, Naive bayes classifier, Domain based weight, Weights, Posterior probability, UCI machine learning repository, Prediction.

Journal ArticleDOI
TL;DR: The objective of this paper is to find the smallest subset of features that can ensure highly accurate classification of breast cancer as either benign or malignant.
Abstract: Breast cancer is one of the second leading causes of cancerdeath in women. Despite the fact that cancer is preventable and curable in primary stages, the huge number of patients are diagnosed with cancer very late. Conventional methods of detecting and diagnosing cancer mainly depend on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the later stages of cancer [1]. The objective of this paper is to find the smallest subset of features that can ensure highly accurate classification of breast cancer as either benign or malignant. Then a comparative study on different cancer classification approaches viz. Naïve Bayes, Support Vector Machine and Ensemble classifiers is conducted where the time complexity of each of the classifier is also measured. Here, Naïve Bayes classifier is concluded as the best classifier with lowest time complexity as compared to the other two classifiers.


Journal ArticleDOI
TL;DR: The paper comprises of a brief study in feeding techniques, parasitic patch elements, introduction of slots, dual feed, shorting pin, air gap and recently introduced concept of defective ground structure that enhances the gain and bandwidth of antenna without increasing its height.
Abstract: Today antenna designers are paying more focus on microstrip patch antennas, because of its numerous advantages in field of communication, such as high reliability, light weight, ease of fabrication etc. But despite of its bountiful advantages, patch antennas also experience some drawbacks viz low gain and narrow bandwidth. These drawbacks can be overcome by taking care of some parameters in the design of antennas. There are numerous designing factors affecting the radiating characteristics of antenna such as patch height, feeding techniques, substrate used in manufacturing of antenna etc. The paper is focused on various bandwidth enhancement techniques. The paper comprises of a brief study in feeding techniques, parasitic patch elements, introduction of slots, dual feed, shorting pin, air gap and recently introduced concept of defective ground structure that enhances the gain and bandwidth of antenna without increasing its height.

Journal ArticleDOI
TL;DR: This paper presents recent updates on papers related to classification of sentiment analysis of implemented various approaches and algorithms to give idea about that careful feature selection and existing classification approaches can give better accuracy.
Abstract: Sentiment analysis is an ongoing research area in the field of text mining. People post their review in form of unstructured data so opinion extraction provides overall opinion of reviews so it does best job for customer, people, organization etc. The main aim of this paper is to find out approaches that generate output with good accuracy. This paper presents recent updates on papers related to classification of sentiment analysis of implemented various approaches and algorithms. The main contribution of this paper is to give idea about that careful feature selection and existing classification approaches can give better accuracy.

Journal ArticleDOI
TL;DR: The design and implementation with a prototype of Reservation-based Smart Parking System (RSPS) that permits drivers to effectively locate and withhold the vacant parking spaces in mentioned and has the potential to smoothen the operations of parking systems, as well as mitigate traffic congestion caused by searching for parking.
Abstract: Locating a parking space in central city areas, especially during the peak hours, is cumbersome for drivers. The issue arises from not having the knowledge of where the available spaces may be at the time, even if known, many vehicles may seek very limited parking spaces to cause severe traffic congestion. In this paper the design and implementation with a prototype of Reservation-based Smart Parking System (RSPS) that permits drivers to effectively locate and withhold the vacant parking spaces in mentioned. This system use cluster based algorithm which helps in periodically learning the parking status from the sensor networks deployed in parking spaces, the reservation service is influenced by the change of parking status. The drivers are allowed to access this said cyber-physical system with their personal communication devices. The system implemented is cost efficient smart parking system for multi-level parking facility using WSN (IR Sensor) and develop an android based application, by cluster based allocation method and performs automatic billing process. The system monitors the availability of idle parking slots and guides the vehicle to the nearest free slot. Cost is minimized by keeping the number of sensors low without sacrificing the reliability. Energy consumption of each mote is kept in check by allowing the systems to sleep periodically and by reducing their communication range. This system‟s reservation-based parking policy has the potential to smoothen the operations of parking systems, as well as mitigate traffic congestion caused by searching for parking.

Journal ArticleDOI
TL;DR: This paper attempts to review the various techniques and their usage of the Automatic Number Plate Recognition System (ANPR) in India and finds its accuracy was found to be 80.8% for Indian number plates.
Abstract: growing affluence of urban India has made the ownership of vehicles a necessity. This has resulted in an unexpected civic problem - that of traffic control and vehicle identification. Parking areas have become overstressed due to the growing numbers of vehicles on the roads today. The Automatic Number Plate Recognition System (ANPR) plays an important role in addressing these issues as its application ranges from parking admission to monitoring urban traffic and to tracking automobile thefts. There are numerous ANPR systems available today which are based on different methodologies. In this paper, we attempt to review the various techniques and their usage. The ANPR system has been implemented using template Matching and its accuracy was found to be 80.8% for Indian number plates.


Journal ArticleDOI
TL;DR: This paper gives brief description of applications and methods where template matching methods were used in versatile fields like image processing, signal processing, video compression and pattern recognition.
Abstract: The recognition and classification of objects in images is a emerging trend within the discipline of computer vision community. A general image processing problem is to decide the vicinity of an object by means of a template once the scale and rotation of the true target are unknown. Template is primarily a sub-part of an object that‟s to be matched amongst entirely different objects. The techniques of template matching are flexible and generally easy to make use of, that makes it one amongst the most famous strategies of object localization. Template matching is carried out in versatile fields like image processing,signal processing, video compression and pattern recognition.This paper gives brief description of applications and methods where template matching methods were used. General Terms Template matching,computer vision,image processing,object recognition.

Journal ArticleDOI
TL;DR: A simplified parts and MRO services flow in the aeronautical industry, characterizing two important stakeholders: customers and repair shops is analyzed, indicating that there is a tendency for the market to build partnerships between stakeholders to expand market penetration.
Abstract: The purpose of this paper is to present the maintenance, repair and overhaul (MRO) and aeronautical industry literature review, providing insights related to strategies of MRO business models. The fundamentals of MRO services and the aeronautical industry have been identified through an extensive literature review. The impact of the MRO outsourcing model was then investigated from the perspective of each stakeholder (aircraft original equipment manufacturers OEMs, repair shops, system suppliers and airlines) using a SWOT (strengths, weaknesses, opportunities and threats) analysis. First, MRO basic concepts were identified: how FAA (Federal Aviation Administration) classifies repair and how MRO is performed. This study also analyzed a simplified parts and MRO services flow in the aeronautical industry, characterizing two important stakeholders: customers and repair shops. Although the production parts purchasing process is fairly simple, the spare parts process requires more attention due to the many players involved. Finally, the SWOT analysis identified strong competition between stakeholders; however, the investigation indicates that there is a tendency for the market to build partnerships between stakeholders to expand market penetration.

Journal ArticleDOI
TL;DR: This work has preprocessed the dataset to convert unstructured reviews into structured form and used lexicon based approach to convert structured review into numerical score value and compared performance of all classifier with respect to accuracy.
Abstract: Social media is a popular network through which user can share their reviews about various topics, news, products etc. People use internet to access or update reviews so it is necessary to express opinion. Sentiment analysis is to classify these reviews based on its opinion as either positive or negative category. First we have preprocessed the dataset to convert unstructured reviews into structured form. Then we have used lexicon based approach to convert structured review into numerical score value. In lexicon based approach we have preprocessed dataset using feature selection and semantic analysis. Stop word removal, stemming, POS tagging and calculating sentiment score with help of SentiWordNet dictionary have been done in preprocessing part. Then we have applied classification algorithm to classify opinion as either positive or negative. Support vector machine algorithm is used to classify reviews where RBF kernel SVM is modified by its hyper parameters which are soft margin constant C , Gamma γ. So optimized SVM gives good result than SVM and naïve bayes. At last we have compared performance of all classifier with respect to accuracy.

Journal ArticleDOI
TL;DR: Smart precision based agriculture makes use of wireless sensor networks to monitor the agricultural environment, thus providing a greenhouse condition for the plants and developing the country stature hugely.
Abstract: Smart precision based agriculture makes use of wireless sensor networks to monitor the agricultural environment. Zigbee and raspberry pi-based agriculture monitoring system serves as a reliable and efficient method for monitoring agricultural parameters. Wireless monitoring of field not only allows user to reduce the human power, but it also allows user to see accurate changes in it. It focuses on developing devices and tools to manage, display and alert the users using the advantages of a wireless sensor network system. A smart system based on precision agriculture would pave the way to a new revolution in agriculture. The user can monitor the agriculture environment from a remote location, thus providing a greenhouse condition for the plants. India being an agro based economy; precision agriculture can bring about an improvement in the primitive methods, thus developing the country stature hugely. General Terms Sensor networks, smart agriculture.

Journal ArticleDOI
TL;DR: Computer aided method for segmentation of brain tumor tissue with accuracy comparable to manual segmentation based on the combination of three algorithms is used.
Abstract: Tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and they have different characteristics and different treatment. As it is known, brain tumor is inherently serious and life threatening. Brain tumor analysis is done by doctors but its grading gives different conclusion which may vary from one doctor to another. However this method of detection resists the accurate determination of size of tumor. To avoid that, uses computer aided method for segmentation of brain tumor based on the combination of three algorithms. This algorithm allows the segmentation of tumor tissue with accuracy comparable to manual segmentation. It also reduces time analysis. At the end of the process the tumor is extracted for MR image and its exact position and its shape is also determined.

Journal ArticleDOI
TL;DR: A new method based on Thresholding along with morphological image analysis techniques to detect brain tumor from MRI image based on intensity enhancement techniques on T1-weighted image which is very much promising compares to other existing method.
Abstract: The functionality of brain can be disrupted by brain tumor, which is an abnormal growth of tissue in brain or central spine. Due to undefined size, shape and location, detection of brain tumor from MRI (Magnetic Resonance Imaging) is a challenging and difficult task. Previous tumor segmentation methods were generally based on intensity enhancement techniques on T1-weighted image, which was appeared with gadolinium contrast agent on strictly uniform intensity patterns. This paper presents a new method based on Thresholding along with morphological image analysis techniques to detect brain tumor from MRI image. The image was first converted to grayscale and then noises were removed by applying different filtering techniques. The grayscale image was then converted to binary image adding 0.3 with the Otsu's threshold value to perfectly segment the tumor region. Afterwards, morphological operations were performed to detect the tumor that contains the brightest part of the MRI. The method suggested for detection was tested over 72 FLAIR images of 72 patients taken from BRATS Brain Tumor database, out of which the proposed algorithm was able to detect tumor from 61 images successfully. Experimental result showed an accuracy rate of 84.72% in detecting 61 patients Brain Tumor which is very much promising compares to other existing method. General Terms Image Segmentation, Medical Image Analysis, Digital Image Processing.

Journal ArticleDOI
TL;DR: Insight is given into the various models and techniques used in estimating cost of the software and it is suggested that a combination of the methods should be used to get an accurate cost estimate.
Abstract: industry of software should be efficient. Due to rapid change in technology, implementation of complex software systems at cheaper cost and the urge to maintain better quality software are some of the major challenges for the software companies. One of the toughest works is cost estimation, in the field of software engineering. It is the estimation of total cost required in developing software. Researchers have proposed various methods of cost estimation. This paper gives an insight into the various models and techniques used in estimating cost of the software. The benefits and drawbacks of the existing cost estimating techniques have been highlighted in this paper. There is as such not any single method which can be regarded as the best method so in this paper it is suggested that a combination of the methods should be used to get an accurate cost estimate.