International Journal of Knowledge Engineering and Soft Data Paradigms (7 papers in press)
Sentiment Classifier Using Unlabeled Data with Emoticon Classification
by Surya Kumari Sampangi, Anjan Babu G
Abstract: Sentiment analysis is the part of opinion mining used to discover the variations of user mood. Generally sentiment analysis deals with feature extraction and sentiment classification, most of the analysis is done by using text mining, mostly training classifiers on labelled data. Emoticon reactions become a major means of communication in social media, where they express the emotions and provide non-verbal communication. This paper propose a classifier making use of emoticons and unsupervised learning, namely K-means clustering, to provide sentiment analysis in an automated matter. The proposed method is trained using data with emoticon signals collected from Facebook and evaluated on 6 different sentiment analysis data sets. Accuracy and ARI metrics are used for evaluation and findings are positive: The classifier outperforms K-means clustering and Sentistrenght2 algorithm in accuracy and training time is correlated to emoticons instead of text features, which is an order less.
Keywords: SECA- Sentiment Emoticon Clustering Algorithm;\r\nARI- Adjusted Rand Index; \r\nTokenization;\r\nLemmatization;\r\nTF-IDF.
A survey on distributed mobile agents' system security
by Yousra Berguig, Jalal Laassiri, Salahddine Krit
Abstract: A multi-agent system (MAS) is a system in which there is a cooperation of autonomous entities called "agents, with intelligent behavior, and have the power to coordinate their goals and action plans to solve a problem or achieve an objective. They are widely applied in telecommunications and e-commerce. In this paper we investigate the security of the distributed mobile agents system. We also discuss the problem of availability of a mobile agent while exposing an overview about the DDoS which is the major attack that threatens the availability in a system. Furthermore, we establish a method to guarantee the confidentiality and secure mobility of a mobile agent during its migration in a distributed system by combining the principle of serialization and cryptography.
Keywords: Mobile Agents; Security; Jade; Cryptography; Serialization; Network Security Protocol; Security Mechanisms; DDoS attack; Distributed systems; Communication security; Mobility.
Special Issue on: BDCA'18 Special Issue Intelligence and Data Management
Visual Content Summarization for Instructional Videos Using Adaboost and SIFT
by Zaynab El Khattabi, Youness Tabii, Abdelhamid Benkaddour
Abstract: Research contributions in video retrieval field are rising to propose solutions for automatic understanding and retrieval of video content. The aim is to make the user able to retrieve specific video sequences in a large database, based on semantic information. In this paper, we process a special case of videos, instructional videos, where text presents very rich semantic information for understanding video content. Indeed, lecture videos are the source of information used in learning systems by educators and students for archiving and sharing knowledge. However, users usually have difficulties to access accurate parts in instructional videos. In our paper, we propose a method to summarize the visual content in instructional videos. For that, first, we segment the video into shots based on SIFT. Then, key frames which are rich in text and figures are extracted from each shot based on entropy measurement. These keyframes are classified using AdaBoost to eliminate non-text frames. The text content in the lecture video summary can be detected and recognized to identify keywords for indexing and classification.
Keywords: Video summarization; Instructional videos; Adaboost; SIFT; Entropy.
Learning Combined Features for Automatic Facial Expression Recognition
by Nabila ZRIRA, Mehdi Abouzahir, El Houssine Bouyakhf, Ibtissam Benmiloud, Majid Mohammed Himmi
Abstract: Facial expressions are one of the most natural and powerful means for the human being in his social communications, whether to share his internal emotional states or to display his moods or intentions, which, in fact, may be true or simply played in a theatrical way. Given the numerous and variety of applications that can be easily planned, building a system able to automatically recognizing facial expressions from images has been an intense field of study in recent years. In this paper, we propose a new framework for automatic facial expression recognition based on combined features and deep learning method.
Before the feature extraction, we use Haar feature based cascade classifier in order to detect then crop the face in the images. Next, we extract Pyramid of Histogram of Gradients (PHOG) as shape descriptors and Local Binary Patterns (LBP) as appearance features to form hybrid feature vectors. Finally, we use those vectors for training deep learning algorithm called Deep Belief Network (DBN). The
experimental results on publicly available datasets show promising accuracy in
recognizing all expression classes, even for experiments which are evaluated on
more than seven basic expressions.
Keywords: Facial expressions; Haar features; PHOG; LBP; DBN.
An efficient Similarity search using a combination between descriptors: a case of study in human face recognition
by Nawfal EL MALIKI, Hassan Silkan, Mounir EL Maghri
Abstract: Face recognition is one of the important fields of search in computer vision. Its principle consists to look for images that represent the similar faces to a given face image the image request. This process is done by extracting a set of features of the request image then making comparison between features generated by the request one and the others extracted from whole face image database. Recently, numerous face representation and classification methods have been proposed in the literature. Nevertheless, many problems related to indexing, combination of adequate descriptors and time computing has not been resolved yet. In this paper, we deal with problems related to features combination and that by conceiving a preformat content-based image retrieval that is mainly oriented to handle face authentication challenges. Its convivial interface allows to user the selection of appropriate weighting coefficient values associate to each feature based on human judgment in order to enhance the retrieval performance. We have tested our proposed method on ORL database by using a set of known features. The obtained results show the performance of our proposed method.
Keywords: face recognition ; principal component analysis ; local Binary Patterns ; features; combination; CBIR ; HOG; Fourier ; Distance ; combination.
Fuzzy detection Orange Tree Leaves Diseases Using a Co-occurrence Matrix Based K nearest neighbors classifiers
by Fatimazahra Jakjoud, Anas Hatim, Abella Bouaaddi
Abstract: The improvement of production efficiency in the farming environment is nowadays of a great interest in the agriculture industry. Several techniques and technologies are used to overcome the main problems facing the developments on this field. In this paper the farms image processing based automatic monitoring is treated. We propose an image processing approach for lemon tree leaves diseases. Our approach is a combination of a fuzzy decision maker and KNN classifiers based on Haralick parameters extracted from the co-occurrence matrix. The testing results are very satisfactory and the efficiency of our system could reach aver than 90% for a limited database.
Keywords: Leaf disease detection;Orange tres leaves; Co-occurrence Matrix; K-nearest neighbors;Fuzzy logic.
Role-Based Access Control in BagTrac Application
by Yassir Rouchdi, Kenza Oufaska, Achraf Haibi, Khalid El Yassini, Mohammed Boulmalf
Abstract: the purpose of this study is to enhance RFID application benefits as a luggage tracking system, first, by defining RFID architecture, components, functioning and middleware roles. Secondly, by discussing the implementation of Role-Based Access Control as a tool regulating access to RFID data, therefore making authentication methods more robust and flexible. To eventually presenting our BAG TRAC application, allowing easier manipulation and real-time visualization of the luggage transportation process.
Keywords: RFID; Middleware; BagTrac; RBAC.