Journal of Artificial Intelligence Research & Advances
Journal of Artificial Intelligence Research & Advances (JoAIRA) ISSN: 2395-6720 Click here for complete Editorial Board Scientific Journal Impact Factor (SJIF): 7.464 Journal of Artificial Intelligence Research & Advances (JoAIRA) is a journal focused towards the rapid publication of fundamental research papers on all areas of Artificial Intelligence Research & Advances. It's a triannual journal, started in 2014. Journal DOI no.: 10.37591/JoAIRA Focus and Scope Cover Artificial intelligence and Philosophy Case based reasoning Heuristic searching Intelligent robotics Planning and theories of action Cognitive aspects of AI Robotics Soft computing theory and applications Evolutionary inspired computing Pervasive computing and ambient intelligence Intelligent Web Bioinformatics Neuroinformatics Computing and the mind Cognitive science Non-classical computing and novel computing models DNA computing and quantum computing Natural computing Readership: Graduate, Postgraduate, Research Scholar, Faculties, Institutions, and in IT Companies. Indexing: The Journal is index in DRJI, Google Scholar Submission of Paper: All contributions to the journal are rigorously refereed and are selected on the basis of quality and originality of the work. The journal publishes the most significant new research papers or any other original contribution in the form of reviews and reports on new concepts in all areas pertaining to its scope and research being done in the world, thus ensuring its scientific priority and significance. Manuscripts are invited from academicians, students, research scholars and faculties for publication consideration. Papers are accepted for editorial consideration through email info@stmjournals.com or nikita@stmjournals.com Abbreviation: JoAIRA Frequency: Three issues per year Peer Reviewed Policy Editorial Board Instructions to Authors Publisher: STM Journals A division of: Consortium eLearning Network Private Ltd Address: A-118, 1st Floor, Sector-63, Noida, Uttar Pradesh-201301, India Phone no.: 0120-4746-214/ Email: nikita@stmjournals.com
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AIS-MACA- Z: MACA based Clonal Classifier for Splicing Site, Protein Coding and Promoter Region Identification in Eukaryotes
Bioinformatics incorporates information regarding biological data storage, accessing mechanisms and presentation of characteristics within this data. Most of the problems in bioinformatics and be addressed efficiently by computer techniques. This paper aims at building a classifier based on Multiple Attractor Cellular Automata (MACA) which uses fuzzy logic with version Z to predict splicing site, protein coding and promoter region identification in eukaryotes. It is strengthened with an artificial immune system technique (AIS), Clonal algorithm for choosing rules of best fitness. The proposed classifier can handle DNA sequences of lengths 54,108,162,252,354. This classifier gives the exact boundaries of both protein and promoter regions with an average accuracy of 90.6%. This classifier can predict the splicing site with 97% accuracy. This classifier was tested with 1, 97,000 data components which were taken from Fickett & Toung , EPDnew, and other sequences from a renowned medical university.
MACA(Multiple Attractor Cellular Automata)
AIS-MACA-Z(Artificial Immune System- Multiple Attractor Cellular Automata-Version Z)
Development of Smart Kitchen System using RFID
We've been implementing an application to keep track of the food inventory i.e. observing the status of the inventory, creation of shopping list automatically without any human interaction based on the food status in the inventory and observing the level of food containers to give an alert message to the user at times when food level is critical. Giving an automation to all these daily routine tasks will reduce human efforts. Proposed system will track the level of food items and based on it the system will create the shopping list and the list will have the only food products at the critical level. Proposed system helps users easily manage kitchen and daily needed food products. Smart Kitchen system using RFID (Radio Frequency Identification) helps in minimizing work load caused in today’s traditional kitchen system. Every Food Product is identified by unique identification number which is being embedded in RFID tag. The old and traditional process of working is being totally replaced by atomizing kitchen system. Proposed system is quite efficient and time and cost of people can be saved. Android is currently all over the market because of its open source nature and almost everyone has an android device. Thus, this paper discusses a mobile application using android which can efficiently handle and manage the kitchen system work.
Messenger RNA Approach for Identifying Inter-nucleotide Signals using Genomic Sequences
The inter-nucleotide signals are a novel way of genomic signal representation of genomic data which is seen to have a discriminatory capability in highlighting the promoter region of gene sequences. The results of applying DFT to in genomic sequence signals that they can discriminate using messenger RNA for identifying inter nucleotide sequences. Genomic Signal Processing (GSP) applications in bioinformatics research have received great attention in recent years, where new effective methods for genomic sequence analysis, such as the detection of converting the genomic sequences into signals have been developed. The use of GSP principles to analyze genomic sequences that requires defining an adequate representation of the nucleotide by numerical values, converting the nucleotide sequences into nucleotide signals. In this paper we present an approach of GSP algorithm which is used in conversion of genomic sequences into signals and also calculating the peaks at coding regions this article also shows the comparative study at the different peaks at a certain elapsed time. The position of these peaks helps in identifying the motifs and the functional behavior of a gene for identifying the Bio Marker selection using a discrete Fourier transform approach
An Implementation of Hospital Management System for Casualty based on Artificial Intelligence
This paper proposes Hospital Management System for Casualty Based on AI. Artificial Intelligence based casualty system provides decision making functionality without human intervention. These days thousands of people die as they do not get medical care within critical time. To get medical facility to the patient on time we have designed this system. Our system uses ANN Algorithm and kohonen’s Algorithm which uses neural network theory for proper decision making and for providing the alerts simultaneously to doctors, ambulance, blood bank, police station and others, for providing medical facilities based on type of casualty in time critical instant.
Visual Cryptography Using Novel Algorithms
Abstract Encryption Schemes on images has tremendously increased to meet the demand of secure image transmission over various mediums such as Wired and Wireless Network. Various encryption standards show low efficiency over image encryption and have less impact on efficiency when the size of image is huge. Nowadays, secret communication plays a major role in many fields of mutual life, such as industry, banking and e-commerce etc. Due to the improvement in network expertise, information security becomes a current application of multimedia technology and swelling communication skill of network progressively leads us to obtain information directly and evidently through images. Hence, data security has become a serious issue. It is a crucial concern that proper encryption decryption should be applied to transmit the data from one place to another place across the internet in order to prevent unauthorized access. Image Cryptography is a special kind of encryption techniques to hide data in an image for encryption and decryption of original message based on some key value. Very few algorithms, provides computational hardness and it makes difficult to break a key to find the original message. In this paper, a variant of the RSA algorithm is used to encrypt the image files to enhance the security in the communication area for data transmission. An image file is selected to perform encryption and decryption using key generation technique. Keywords: Visual Cryptography, Encryption, RSA Algorithm Cite this Article Diptanu Basak, Abhishek Das. Visual Cryptography Using Novel Algorithms. Journal of Artificial Intelligence Research & Advances. 2017; 4(3):1–10p.
Actors, Agents and Oracles in the Context of Artificial Intelligence
Abstract In this paper, we further develop the action theory of stratified hierarchic intelligence oriented at the creation of artificial intelligence. In this framework, we analyze and study the concept of an actor extending this concept and the corresponding model from specifically computational context to systems theory. The goal is to explore and use actor model for intelligent systems. The next step in the development of the action theory of hierarchic intelligence is investigating agents as a specific kind of actors with the main emphasis on oracles as imperative actors and empowered agents in intelligent interactions. Keywords: Artificial Intelligence, superintelligent, distributed intelligent managed element (DIME) Cite this Article Mark Burgin. Actors, Agents and Oracles in the Context of Artificial Intelligence. Journal of Artificial Intelligence Research & Advances. 2017; 4(3): 17–25p.
An Unsupervised Common Sense-based Learning Framework for Emotion Detection and Classification in Textual Social Data
Abstract Emotions are inherent to human behavior and generally expressed in response to the stimuli from the external environment or some internal processes. With the widespread use of online social platforms, people often express themselves using tweets, blogs and posts which generally include affective text. The topic of emotion detection and recognition has been widely studied in computational science and in fact a separate area “Affective Computing” is dedicated to its study. Although several techniques have been used for the purpose of detecting emotions from text, each technique has its own strengths and weaknesses. Our study focuses on employing lexical, semantic and real world information to detect six basic emotions from text. We propose a hybrid framework, for detecting and classifying emotions, which initially builds up the emotion lexicon, uses semantic information for grouping together related words and employs, a common sense platform, ConceptNet, to make the classification more accurate. The results obtained, utilizing this framework are quite encouraging and comparable to state-of-the-art techniques available. Keywords: Emotion detection, hybrid learning, affective computing, social data, real-world knowledge Cite this Article Abid Hussain Wani, Rana Hashmy. An Unsupervised Common Sense-based Learning Framework for Emotion Detection and Classification in Textual Social Data. Journal of Artificial Intelligence Research & Advances. 2017; 4(3): 49–56p.
Constraints and Limitationsin Software Reliability Prediction
Abstract Software, as entered into our lives in all aspects. We are now so dependent on technology that it has become very critical for our lives. All the modern technological development, be it in any field incorporates the usage of software directly or indirectly. Due to this the need for reliable software is also gaining importance. A software in deeply tested before being brought into its professional application but still it is very difficult to guarantee its reliability and own that its is 100 percent free from bugs and will not lead to failures. In this paper we have discussed various aspects of software development which are very critical for its reliability and the various limitations associated with the true prediction of the degree to reliability of the software. There is always a possibility that the Software may have latent bugs even after passing all levels of test. There has been a lot of research in this area and statisticians have given a number ofsoftware reliability growth models for reliability prediction, still there are issues while choosing the appropriate model and making a compliance with the standard assumptions of the selected models. Due to this reliability prediction accuracy is still a complex area. Keywords: Calender time, error, failure data, failure rate, hardware reliability, software reliability Cite this Article Archana Kumar, Abhinav Juneja, Sapna Bajaj. Constraints and Limitations in Software Reliability Prediction. Journal of Artificial Intelligence Research & Advances. 2017; 4(3): 6–10p.
Artificial Intelligence for Better Climate Governance
Abstract The world’s attention today is on climate change that is no more just a perceived risk. Climate change is already impacting livelihoods; and by impacting the vulnerable regions and the communities more than others, may also be aggravating economic and social inequalities. Effective climate governance, therefore, requires better data collation and analysis thereof. Early warning signals of the impending change must not only be received but also distributed through strong institutional frameworks. While significant progress is evident in this direction, the limitations of those in charge of governance have also been evident. Due to this, increasingly, there is a trend of inter-governmental and inter-organizational partnerships along with higher engagement of communities to better adapt to climate change. Keywords: Artificial intelligence, climate change, climate governance Cite this Article Satinder Bhatia. Artificial Intelligence for Better Climate Governance. Journal of Artificial Intelligence Research & Advances. 2017; 4(3): 37–43p.
A Review Paper on Comparison of Algorithms for Supervised Machine Learning
Abstract Supervised learning attempts to create calculations that help deliver general speculations, which is able to do forecasts about future occasions. The objective of supervised learning is to manufacture a model of the segregation of class names in terms of predictor components. The classifier produced accordingly is then used to allocate class marks to the testing data where the estimations of the indicator elements are known, yet the estimation of the class name is not known. This paper compares different supervised machine learning characterization strategies. The algorithms that are taken into considerations are K-Nearest Neighbor, Linear Regression, Logistic Regression, Naïve Bayes, Decision Trees, Random Forests and Neural Networks. Keywords: Supervised machine learning, algorithm, K-Nearest Neighbor, Linear Regression Cite this Article Abhishek Panjabi, Krupali H. Shah, Vyshali Lasitha et al. A Review Paper on Comparison of Algorithms for Supervised Machine Learning. Journal of Artificial Intelligence Research & Advances. 2017; 4(3): 11–16p.
Superpixel Segmentation using DBSCAN Clustering Algorithm
Abstract Superpixel division strategies are generally utilized as a pre-preparing venture to accelerate picture handling tasks. They assemble the pixels of a picture into homogeneous locales while attempting to regard existing shapes. In this paper, we propose a quick superpixels division calculation with contour adherence utilizing the density based spatial clustering of applications with noise (DBSCAN) algorithm. We implement a two-stage processing to decrease the computational costs of superpixel algorithms. First is clustering stage and the second is merging stage. At first, the DBSCAN algorithm with color similarity and geometric restrictions are used to cluster the pixels quickly, and then, small clusters are merged into superpixels by their neighborhood. It is done using a distance, which is defined by color and spatial features. We have defined a robust and simple distance function to obtain better superpixels. The experimental results show that our proposal outperforms the state-of-the-art superpixel segmentation methods in terms of both accuracy and efficiency. Keywords: Superpixels, DBSCAN, segmentation, under segmentation error, boundary recall Cite this Article Partha Ghosh, Kalyani Mali, Sitansu K. Das Superpixel Segmentation using DBSCAN Clustering Algorithm. Journal of Artificial Intelligence Research & Advances. 2017; 4(3): 26–36p.
A Survey Paper on Offline Signature Recognition with Various Techniques
Abstract The signature recognition (SR) and verification technique is used to appreciate and confirm character’s handwritten signature. Nowadays handwritten signature is among the most commonly permitted personal attributes for identification verification. Signature verification provides authorization in financial and business transaction. Signature verification finds its application within the area of internet banking, passport verification approach, provides authentication to candidates in public examination from their signatures, credit cards, bank cheques, etc. Due to the growing amount of handwritten signatures, it is vitally fundamental that a person offline handwritten signature to be identified uniquely. A signature is a behavioral biometric characterized by way of behavioral trait that a creator learns and acquires over a period of time and turns into his specific identification. Keywords: Image, signature recognition, template matching, neural networks, support vector machine Cite this Article Priyanka Chauhan, Nirupma Tiwari. A Survey Paper on Offline Signature Recognition with Various Techniques. Journal of Artificial Intelligence Research & Advances. 2017; 4(2): 19–29p.
Study and Analysis of Domain Based Digital Image Water Marking Techniques: Survey
Abstract Due to the modern steps forward in internet technology and evolution of very high-speed networks operating everywhere, security of digital content is must. So, it has become a hard job to protect copyright of an individual's creation. Digital image watermarking (DIW) is usually utilized for copyright protection of virtual facts. The effectiveness of a digital watermarking (DW) approach is indicated by using the robustness of embedded watermarks (EW) against the quantity of assaults. In the present paper, a survey on DW procedure, applications, concept and its offerings in various fields are introduced. There are lots of strategies for watermarking like, discrete wavelet transforms (DWT), and discrete cosine transform (DCT). The ordinary performance aspect of these strategy’s peak signal to noise ratio (PSNR), imply rectangular errors (MSE). Keywords: Digital wartermaring, digital watermarking techniques, DWT, DCT etc. Cite this Article Vipin Agrawal, Karuna Markam. Study and Analysis of Domain Based Digital Image Water Marking Techniques: Survey. Journal of Artificial Intelligence Research & Advances. 2017; 4(2): 39–45p.
Enhance Brain Images using Adaptively Regularized KFCM Approaches on Image Segmentation: A Survey
Abstract The purpose of image segmentation is to apply logic and convert the explication of an image into some aspect that is easy for examine. This paper concerns on study of various image segmentation (IS) techniques and analysis. The merits and demerits are also described in this paper. Then there is discussion about various underpinning concerns in IS and find the gap between them that require to be connected so as to refine the IS performance and effectiveness. Keywords: Image segmentation, FCM, MRF, KFCM, thresholdbased, edge based, region based many other techniques Cite this Article Yogesh Kumar Gupta, Nirupama Tiwari, Vivek Jain. Enhance Brain Images using Adaptively Regularized KFCM Approaches on Image Segmentation: A Survey. Journal of Artificial Intelligence Research & Advances. 2017; 4(2): 30–38p.
A Survey Paper on Image Denoising Technique for Satellite Images
Abstract Image denoising (ID) is a material issue found in various image processing (IP) and PC vision issues. There are numerous techniques of denoising images. The most necessary property of ID model is that it ought to totally diminish clamor, however much as could reasonably be expected and edge conservation. This paper gives a knowledge perspective of some real work in the field of ID. Specialists have distributed numerous calculations in the field of ID which have numerous presumptions, focal points and in addition, constraints. We are using satellite images for removing Gaussian noise that are type of image denoising. Satellite images are corrupted by noise in its acquisition and transmission. After brief presentation, different techniques have been explained for removing noise. Keywords: Image denoising, filtering, noise models, linear and non-linear filters, Gaussian filter Cite this Article Roopali Arya, Nirupama Tiwari. A Survey Paper on Image Denoising Technique for Satellite Images. Journal of Artificial Intelligence Research & Advances. 2017; 4(2): 1–9p.
Unstructured Data Analysis with the Use of Cognitive Capabilities of Watson Analytics
Abstract Intellectual science concentrates on how individuals investigate for data when connecting with the web. The exploratory hunt refines search exercises and updates the inward portrayals of the inquiry issues. Exploratory data recovery frequently includes badly characterized search objectives and advancing criteria for assessment of pertinence. The associations amongst people and the data framework include more intellectual movement, and frameworks that bolster exploratory pursuit along these lines, need to consider the subjective complexities required amid the dynamic data recovery prepare. Building adaptable psychological model to investigate unstructured information is testing process. IBM’s Bulemix cloud Watson administrations help individuals to fabricate extreme intellectual space. It fabricates psychological, cloud-based investigation applications by opening significant bits of knowledge covered up in unstructured information, including restrictive information, and also open information. The proposed application uses the utilization of Watson’s Discovery administration to concentrate later and important data of an organization by questioning in common dialect. Keywords: Cognitive computing, Watson analytics, discovery service, natural language processing Cite this Article Sindhu B. Jigali, Nirmala CR. Unstructured Data Analysis with the Use of Cognitive Capabilities of Watson Analytics. Journal of Artificial Intelligence Research & Advances. 2017; 4(2): 10–18p.
A Review of Artificial Intelligence with Psychological Framework
Abstract This appeared as if AI were one of the center inquiries of science, on an equivalent balance with inquiries concerning the way of the universe and the way of life. Computerized reasoning, with its computational similitude, offered the main clear course to tackle this testing issue, and the advances made in the field’s initial 20 years, since it is establishing at the 1956 Dartmouth meeting, appeared to be sufficiently great to guarantee quick walks toward an expansive computational hypothesis of mental marvels. In this paper, I audited the suppositions, the reasons they appeared well and good, and the extra reasons; a significant number of them were sociological but they had fallen into disapproval among numerous AI analysts. After this, I consider whether they have a part to play later of the field and, provided that this is true, how we can energize their expanded usage. Keywords: Artificial intelligence, DARPA, cognitive system Cite this Article Shruti Mehrotra, Aviral Shukla. A Review of Artificial Intelligence with Psychological Framework. Journal of Artificial Intelligence Research & Advances. 2017; 4(1): 15–20p.
Natural language processing is a branch of software engineering and artificial intelligence which is varied with association amongst PCs and human dialects. Natural language processing is the investigation of scientific and computational demonstration of different parts of dialect and the improvement of an extensive variety of frameworks. These incorporate the spoken language frameworks that coordinate discourse and natural language. Natural language processing is a part in software engineering because numerous parts of the field manage semantic components of calculation. Natural language processing is a territory of research and application that investigates how PCs can be utilized to comprehend and control normal language or discourse to do helpful things. The utilizations of natural language processing incorporate fields of study, for example, machine interpretation, natural language text processing and outline, UIs, multilingual and cross language information retrieval (CLIR), speech recognition, artificial intelligence (AI) and master frameworks. Keywords: Natural language processing (NLP), cross language information retrieval (CLIR), artificial intelligence (AI) Cite this Article Ishan Pandita. NLP Related to Artificial Intelligence. Journal of Artificial Intelligence Research & Advances. 2017; 4(1): 21–26p.
Review Paper on Efficient Quality Inspection of Food Products Using Neural Network Classification
Abstract With the increasing competiveness in the field of food production growing at a faster pace across the globe, quality is one of the most desirable features that a product should possess. The ability to produce quality as well as safety food is the most prerequisite factors for both, national and international market. This paper explains the recently developed approaches and latest research efforts related to the assessment of the quality of different food products through comparison of multivariate techniques and examine the potential for their deployment. Here, there is a comparison of the abilities of nine different multivariate classification methods: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), soft independent modeling of class analogy (SIMCA), partial least squares (PLS) classification, K-nearest neighbor (KNN), support vector machines (SVM), probabilistic neural network (PNN), and multilayer perceptron (ANN-MLP). The overall results sufficiently demonstrate the fact that the probabilistic neural network (PNN) method has the potential to determine the quality of various food products significantly with high accuracy. Keywords: Food quality inspection, artificial intelligence techniques, wavelet transform, probabilistic neural network (PNN) approaches Cite this Article Syed Sumera Ali, Sayyad Ajij D. Review Paper on Efficient Quality Inspection of Food Products Using Neural Network Classification. Journal of Artificial Intelligence Research & Advances. 2017; 4(1): 1–14p.
Review Paper for Different Techniques Using Image Enhancement
Abstract Image enhancement (IE) performs a basic position in vision purposes. Lately, plenty of work has been carried out in the discipline of IE. Many tactics have already been proposed until now for reinforcing the digital pictures. This paper has presented a comparative analysis of quite several IE methods. This paper has proven that the fuzzy logic (FL) and histogram founded tactics have fairly amazing outcome more than the obtainable systems. This paper finally ends up with suitable future recommendations to increase fuzzy founded IE extra. Keywords: Fuzzy logic (FL), image processing (IP), color image enhancement, histogram equalization (HE) Cite this Article Saira Baghel, Priya Pathak, Sandeep Gupta. Review Paper for Different Techniques Using Image Enhancement. Journal of Artificial Intelligence Research & Advances. 2017; 4(1): 27–32p.
Survey Paper of Image Forensics based on Tampering Detection using Feature Extraction Technique
Abstract The digital images (DI) are becoming vital components in the field of statistics forensics and protection. Because of the popularity of picture enhancing tools, DI may be altered. Therefore, it is ought to create forensic strategies that are able to detect tampering in pictures. Image forensic (IF) is a vital part of many investigations. The numerous low-cost effective digital tools have enabled easy picture creation, change and distribution, which make fraudulent picture forgeries simpler than ever. In this paper, we used to feature extraction techniques. Keywords: Image forensic, image tampering, feature extraction techniques Cite this Article Saira Baghel, Priya Pathak, Sandeep Gupta. Survey Paper of Image Forensics Based on Tampering Detection Using Feature Extraction Technique. Journal of Artificial Intelligence Research & Advances. 2017; 4(1): 33–42p.
Abstract The essential thought of the undertaking is to fabricate Goggle by utilizing human machine interface which can be utilized to control mouse by utilizing head-tilt and eye-flicker. This mouse-copying gadget can be observed to be most helpful by physically debilitated and incapacitated persons who can no longer control the PCs by utilizing their hands. Since the gadget depends on person's head and eye movements, it can be utilized even by patients who are incapacitated from below the shoulder. There is less energy required for head movement and eye flickering. That's why user won't get drained from utilizing this gadget. In this gadget or goggle we are utilizing 2D-accelerometer for identifying the head movement, by this, the movement of the cursor has been finished. A photograph sensor distinguishes eye flickering. The infrared handset comprises of a 935 nm IR transmitter and a phototransistor mounted on a similar unit. This identifies a solid increment in the reflected signal upon deliberate long squint when contrasted with ordinary eye flicker. Duplicate hands and legs have been ended up being very helpful and palatable for a set number of exercises. Presently, computer can be easily found at home. The disappointment of these manufactured appendages in empowering the amputees to effectively handle a PC is obvious since one can practice a constrained control on the mechanical appendages connected. Besides, numerous amputees have an occupation and work in workplaces wherein they need to utilize a PC. Consequently, gadget ought to be considered which would permit simple and cover control up PC. Keywords: Handicapped, mouse, Goggle, accelerometer, micro controller Cite this Article Afjal Ahmad, Anas Ahmad Khan, Qazi Ahmad Saeed. Goggle Mouse. Journal of Artificial Intelligence Research & Advances. 2016; 3(3): 31–36p.
An Introduction on Virtual Reality and Its Challenges
Abstract In the last few years virtual reality has become most popular than other technologies. In this paper, a short introduction of virtual reality, basic terminology, present and future of virtual reality and application of this technology in daily life are mentioned. Virtual reality makes users feel in a virtual environment (VE) by using computer hardware and software. Sometimes, virtual reality (VR) is also called virtual environment (VE). Keywords: Virtual reality, Sensorama, HMD, VCASS, BOOM, VR Cite this Article Jagannath Dixit, Shailendra Kumar, Prashant Mishra, et al. An Introduction on Virtual Reality and Its Challenges. Journal of Artificial Intelligence Research & Advances. 2016; 3(3): 1–8p.
Tweet Segmentation for Named Entity Recognition
Abstract Twitter is having lots of users to allocate and distribute a large amount of recent information, various submission in Information Retrieval-IR and Natural Language Processing-NLP undergo harshly through the deafening and tinny kind of tweets. We recommend tweet segmentation framework in a group, called HybridSeg. By dividing tweets with significant segments, the background information is conserved and simply extract with the downstream applications. HybridSeg search the best segmentation of a tweet by increasing the addition of the stickiness score. Two tweet data sets is an experiment; it shows that tweet segmentation quality is extensively increased by learning both global as well as local contexts compared by using global context alone. Additional accuracy is able to name entity recognition by putting segment-based part-of-speech (POS) tagging. Keywords: Information Retrieval, Natural language Processing, Named Entity Recognition (NER), Part of Speech (POS) Cite this Article Krushnadeo Tanaji Belerao, Tweet Segmentation for Named Entity Recognition. Journal of Artificial Intelligence Research & Advances. 2016; 3(3): 22–25p.
Social Media Blog Analysis using Watson Analytics
Abstract Analytics is defined as the discovery, interpretation, and communication of meaningful patterns in data. A blog is a discussion or informational website published on the world wide web consisting of discrete, often informal diary-style text entries and may contain unstructured or semi-structured data. The analysis of unstructured data types is current challenge, getting attention in industry. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization and information privacy. Watson analytics is a cloud based smart data discovery tool from IBM analytics. Watson solutions aim to enhance, scale, and accelerate human expertise, targeting a wide range of complex challenges. AlchemyAPI is a collection of APIs that offer text analysis through natural language processing. The keyword extraction API works on URL’s, HTML documents and plain text. It automatically detects the language of the content and then performs the appropriate analysis. AlchemyAPI’s keyword extraction algorithm employs sophisticated statistical algorithms and natural language processing technology aided with machine learning techniques to analyze the content and identify the relevant keywords. The proposed blog analyzer application uses the Alchemy_API’s keyword extraction service, accepts the URL’s of the blogs and processes the content by employing sophisticated statistical algorithms and natural language processing aided with machine learning techniques, to retrieve the related/relevant and rank the keywords of the input content. Keywords: Blog, keywords, Watson analytics, Alchemy API, keyword extraction service Cite this Article Jigali Sindhu B, Nirmala CR. Social Media Blog Analysis using Watson Analytics. Journal of Artificial Intelligence Research & Advances. 2016; 3(3): 15–21p.