7 | P a g e including Kruger and Grest  and Chakraborty et al. . Action grammars are highly modular but require manual structuring making action grammars impractical for systems intended to classify a large set of action classes. Action templates are a combination of action primitives into one larger representation. Pattern matching is usually applied to compare actions to a collection of action templates in a database. Junejo et al.  propose a view independent approach to action recognition on 2D video sequences using Self Similarity Matrices (SSM). Their approach captures temporal histograms of gradient orientations in the spatial domain and concatenates the features descriptors into one large local SSM feature vector descriptor. This feature vector descriptor is an action template. Yao et al.  collect action pose templates as a combination of Histogram of Gradient (HoG) features and Histogram of Optical Flow (HoF) features. These templates are classified using Support Vector Machines (SVM’s). Action templates are known to be effective and discriminative, but do not have a built-in mechanism to account for temporal segmentation. Temporal statistics find statistical patterns of actions in the temporal domain such as identifying frequent features over time.
Recognition Using Classification and Segmentation Scoring Recognition Using Classification and Segmentation Scoring* Owen Kimball t, Mari Ostendorf t, Robin Rohlicek t Boston University ~ B B N Inc 44[.]
Abstract-There are a lot of techniques relevant for the purpose automated leaf recognition for plant classification. Many algorithms have been introduced in the past decade and achieved good performance. Efforts have focused upon many other fields but properties of features have not been well investigated. A group of features is selected in advance but important feature properties are not well used to feature selection. In this paper the performance of different features extraction methods are compared, different combinations of features and a number of classifiers applied for leaf identification process are also discussed.
Event recognition and classification has been pointed out to be very important to improve com- plex natural language processing (NLP) applica- tions such as automatic summarization (Daniel et al., 2003) and question answering (QA) (Puste- jovsky, 2002). Natural language (NL) texts often describe sequences of events in a time line. In the context of summarization, extracting such events may aid in obtaining better summaries when these have to be focused on specific happenings. In the same manner, the access to such information is crucial for QA systems attempting to address questions about events.
Abstract- Image processing is widely used for food recognition. A lot of different algorithms regarding food identification and classification has been proposed in recent research works. In this paper, we have use an easy and one of the most powerful machine learning technique from the field of deep learning to recognize and classify different categories of fast food images. We have used a pre trained Convolutional Neural Network (CNN) as a feature extractor to train an image category classifier. CNN’s can learn rich feature representations which often perform much better than other handcrafted features such as histogram of oriented gradients (HOG), Local binary patterns (LBP), or speeded up robust features (SURF). A multiclass linear Support Vector Machine (SVM) classifier trained with extracted CNN features is used to classify fast food images to ten different classes. After working on two different benchmark databases, we got the success rate of 99.5% which is higher than the accuracy achieved using bag of features (BoF) and SURF.
In our survey, we found that the deep neural network (DNN) classifier are the new and popular texture extraction methods is giving the highest accuracy and performance (98.7%) for texture classification. Leaf recognition is useful to identify the medical plant leaf type. The methods are used to extract plant leaf features are based on color, shape and texture etc. Classifiers plays significance role to verify the data and survey the accuracy of classification algorithm. Deep neural network (DNN) gives better result as compare to other classifier. To identify different plant leaf images based on its surface parameter is challenging and most expensive task. Plant leaf image surface parameters are color, texture and shape. The combined feature extracted from each of its parameter is used to identify leaf type and gives better result as compare to using single parameter. The time and Frequency domain features computed by Symbolic representation (SAX) are well used in research along with 2DBPE representing a features along with DNN gives higher accuracy to classify the leaf images.
Stern et al.  address the LCS method, a predecessor of DTW. Due to stable dimensional data strings, LCS has an advantage over Euclidean and Manhattan; in addition, LCS similarity measure is sturdy for noises. In LCS, noisy components of path movement will not be compared. Authors propose to use the classification algorithm most discriminating subsegments (MDSs) based on LCS algorithm; hence, named as MDSLCS. The key idea of MDSLCS is the automatic identification and derivation of MDSs which makes it a better classifier than extracting full gestures. Representing each gesture as MDSs is analogous to phonemes in speech or strokes in handwriting. A recognition result of 92.6% was achieved as compared to 89.5% recognition using HMM.
We ran three evaluation modes: dec-1st, dec-all, and semh. Mode dec-1st only evaluates the first deci- sion for each phrase; the baseline in this case is .554 since 55.4% of the first decisions are C. In mode dec-all, we evaluate all decisions that were made in the course of recognizing the semantic head. This mode emphasizes the correct recognition of seman- tic heads in phrases where multiple correct decisions in a row are necessary. We define the confidence for multi-decision classification as the product of the confidence values of all intermediate decisions. There is no obvious baseline for dec-all because the number of decisions depends on the classifier – a classifier whose first decision on a four-word phrase is NC makes one decision, another one may make three. The mode semh evaluates how many semantic heads were recognized correctly. This mode directly evaluates the task of semantic head recognition. The baseline for semh is the tokenizer that always returns the syntactic head; this baseline is .488. 5 Table 4
information security risks. Therefore, it’s imminent to find an effective spam classification solution. Many techniques against spam have been proposed (see below): keyword filtering, black-list, white-list, hashing, rule-based filter, statistical filter. Among them, statistical filter (especially bayesian filter) play a key role in anti-spam product. Spam recognition rate of an outstanding Bayesian recognizer can exceed 99.9%.
Named Entity Recognition and Classification (NERC) has become an important sub-task in the Natural Lan- guage Processing area. It is known that an effective treatment of Named Entities can benefit the perfor- mance of applications like Machine Translation (MT), Information Extraction (IE), Information Retrieval (IR) or Question Answering (QA). In the early stages, NERC systems identified a few types of entities, namely person, organisation and location names. Over time, numerical and temporal expressions have been also considered as identifiable types of entities. Concerning to Basque, there is a NERC system called Eihera (Alegria et al., 2003) that recognises and classifies person, organisation and location names, but it does not deal with numerical entities up to date. The Numerical Entity Recogniser and Classifier for Basque (NuERCB) presented here aims to address this lack.
Figure 1. Basic model of Speech Recognition system A fundamental distinctive unit of a language is a phoneme. Different languages contain different types of phoneme sets. Syllables contain one or more phonemes, while words are formed with one or more syllables, concatenated to form phrases and sentences. One broad classification for English is in terms of vowels, consonants, diphthongs, affricates and semi-vowels . The speech recognition system can be classified by the type of speech. They are continuous speech, isolated word, connected word and spontaneous speech.
namely minimum redundancy and maximum rele- vance , and Markov random fields , were ap- plied to an electrode array by Liu et al. , who used Kullback–Leibler divergence and feature scatter to rate the relevance and redundancy of features. The features were then ranked and selected into sets ac- cording to these ratings. Similarly, Bunderson et al. defined three data quality indices – namely, repeat- ability index (RI), mean semi-principal axis, and sep- arability index (SI) – to evaluate the changes in data quality over repeated recordings of EMG . Classification complexity estimation was not investi- gated in the aforementioned studies, but algorithms intended to quantify attributes relevant to the com- plexity of pattern recognition tasks were introduced.
Named-entity recognition and classification (NERC) is the identification of proper names in text and their classification as different types of named entity (NE), e.g. persons, organisations, locations, etc. This is an important subtask in most language engineering applications, in par- ticular information retrieval and extraction. The lexical resources that are typically included in a NERC system are a lexicon, in the form of gaz- etteer lists, and a grammar, responsible for rec- ognising the entities that are either not in the lexicon or appear in more than one gazetteer lists. The manual adaptation of those two re- sources to a particular domain is time- consuming and in some cases impossible, due to the lack of experts. The exploitation of learning techniques to support this adaptation task has attracted the attention of researchers in language engineering.
Kiruthiga N, Divya E, Haripriya R, Haripriya V., "Real Time Object Recognition and Classification using Deep Learning", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5 Issue 2, pp. 355-359, March- April 2019.
Nowadays, antique coins  are becoming subject to a very large illegitimate trade. Thus, the interest in reliable automatic coin recognition systems in cultural heritage as well as law enforcement institutions rises rapidly. Usual methods to fight the illicit traffic of ancient coins comprise manual, periodical search in auctions catalogues, field search by authority forces and the periodical controls at expert dealers, also a unwieldy and unrewarding internet search, followed by human investigation. Applied pattern recognition algorithms are various ranging from neural networks to eigen spaces, decision trees, edge detection as well as gradient directions, and contour with texture features. Tests performed on image collections both of medieval with indian modern coins show that algorithms performing good quality on Indian modern coins do not necessarily meet the wants for classification of medieval ones. Major difference between ancient and Indian modern coins is that the indian ancient coins  have no rotating symmetry and subsequently their diameter is unknown. Since ancient coins are all too often in very unfortunate conditions, common recognition algorithms can effortlessly fail. The description that most influence the quality of recognition process are yet unexplored. The COINS project addresses this investigation gap and aims to give an efficient image based algorithms for coin categorization as well as identification. There is a basic need of highly perfect and efficient automatic coin recognition systems in our everyday life. Coin recognition systems as well as the coin sorting machines have become an essential part of our life. They are used in banks, vending machines, grocery stores, supermarkets etc. In-spite of daily uses coin recognition systems can also be used for the investigate purpose by the institutes or organizations that deal with the ancient coins. There are three types of coin recognition systems based on dissimilar methods used by them accessible in the market:
In communication system, the classification of modulation plays a decisive role. In the literature , some ideas about the automatic modulation classification are proposed. An automatic classification method based on feature-based digital modulation is outlined. In , an automatic recognition algorithm for communication signal modulation type based on wavelet transform and pattern recognition is proposed. In , the cumulant and the SVM classifier is used for classification. A lot of research on automatic modulation are in literature .
Automatic human facial recognition has been an active reasearch topic with various potential applications. In this paper, we propose effective multi-task deep learning frameworks which can jointly learn represen- tations for three tasks: smile detection, emotion recognition and gender classification. In addition, our frameworks can be learned from multiple sources of data with different kinds of task-specific class labels. The extensive experiments show that our frameworks achieve superior accuracy over recent state-of-the-art methods in all of three tasks on popular benchmarks. We also show that the joint learning helps the tasks with less data considerably benefit from other tasks with richer data.
The two important challenges of clustering analysis is to select a suitable similarity measure to form clusters, and second is to evaluate the quality of the clusters formed. or better saying in another way is to define a criterion function that measures the clustering quality of any partition of the data and then the problem is one of finding the partition that optimizes the criterion function As per the application to face recognition system, clusters are considered as various classes of face images which are given, the traditional unsupervised classification approach is defined as classifying a test sample is nothing but finding based on some similarity measure to which cluster it is most similar to and assigning it to that class with which the cluster has been formed with. But in this paper, a new concept is introduced where the second challenge of evaluating the cluster is mounded as a classification technique. In this concept, a test sample is assigned to a cluster (class) for which criterion function chosen to evaluate the quality of the cluster is optimized when test sample is also considered as one of the sample of that cluster. This simple thought is used to develop a robust face recognition/classification system that is relieved from the problem of over-fitting and is giving high recognition rate as that of computationally complex kernel methods.
Goudelis et al. (2017) , presented a Human action recognition is currently one of the hottest areas in pattern recognition and machine intelligence. Its applications vary from console and exertion gaming and human computer interaction to automated surveillance and assistive environments. We define the notion of a 3D form of the Trace transform on discrete volumes extracted from spatio-temporal image sequences. On a second level, we propose the combination of the novel transform, named 3D Cylindrical Trace Transform, with Selective Spatio- Temporal Interest Points, in a feature extraction scheme called Volumetric Triple Features, which manages to capture the valuable geometrical distribution of interest points in spatio-temporal sequences and to give reputation to their action- discriminate geometrical correlations. The technique provides noise robust, distortion invariant and temporally sensitive features for the classification of human actions
EEG is a non invasive signal with strength about 10-100 μV and it is contaminated with noise. ICA based methods can remove all types artifacts when source signals are independent. Researchers have used one of the artifacts removal methods which are discussed in the paper but it is difficult to remove artifact using single method. In future one can combine traditional method with machine learning to get automatic artifact removal. SEED or DEEP database are commonly used by 70% researchers, which have used 64 or 32 electrodes. If exact number of channels required for emotion recognition are found then number of electrodes can be reduced in practical BCI systems. Machine learning techniques like LDA, kNN are simple to implement but the accuracy obtained by these techniques is about 55% for two state emotions. With SVM we can get emotion classification accuracy up to 70% for two class which has been reduced when user has used multiclass SVM for six emotions except . Though neural network and deep learning has increased the performance of emotion recognition, technical and usability challenges are still in existence. It is observed that DBN have good classification ability. We recommend to use power spectral density or differential entropy features for DBN for better results. CNN architecture has capability to extract complex features of data at each layer to determine output. There is no limit on number of channels to be used in CNN as they are capable of handling large data. In future, CNN can be used as fundamental tool for feature learning and classification. In RNN connections between nodes form a directed graph along the temporal sequence. This is helpful in predicting temporal dynamic behavior. Most RNN studies have used two LSTM layers and one or two fully connected layers for classification. In future, one can vary the number of fully connected layers and find accuracy. It is observed that accuracy has increased above 80% with deep learning. Hybrid models have given promising results for EEG classification but further research is required to check the effectiveness of them. In the experimentation, number of subjects having variation in ages, gender should be increased such that, from training more features are obtained giving better classification accuracy during testing. For a deep neural network to do a good job, create a network and proceed it to optimize its architecture such that we get best solution for a particular problem.