Intelligent Decision Technologies - Volume 13, issue 2

Authors: Huang, Zhi

Article Type: Research Article

Abstract: Feature selection is an effective approach for solving the curse of dimensionality. Evolutionary computation, such as genetic algorithms, are extensively applied into feature selection. However, with the available algorithms, features aren’t screened before evolutionary computation starts and all of them are equal in status during the process of evolutionary computation. In this paper, a new algorithm that screens features before evolutionary computation starts, and makes full use of the screened ones during the process of evolutionary computation is proposed. In detail, important and useful features are found by scoring all features, and endowed with privileges in obtaining advantages comparing to …other features during the forthcoming process of evolutionary computation, which is the first stage of our proposed algorithm. As for the second stage, we design a genetic algorithm with multiple sub populations, in which each sub population corresponds to a combination of important and useful features, and a competition mechanism between sub populations is introduced. As a result, important and useful features are further sufficiently used and extensively explored compared to the available algorithms, hence classification accuracies are increased. Experiments are performed with 8 datasets comparing to 11 state-of-the-art algorithms to validate our proposed algorithm. And the results show that our proposed algorithm outperforms the 11 state-of-the-art algorithms. Show more

Keywords: Feature selection, feature score, feature combination, Genetic Algorithms, multiple sub populations

DOI: 10.3233/IDT-180111

Citation: Intelligent Decision Technologies, vol. 13, no. 2, pp. 139-151, 2019

Authors: Anoop, V.S. | Asharaf, S.

Article Type: Research Article

Abstract: A vast majority of text mining and machine learning algorithms such as topic models, classification, clustering are based on statistical methods thus the semantics or meaning of the words or phrases are not considered. Interpretation of outputs generated by such algorithms are difficult for humans because of the absence of sufficient contextual information. Distributional semantics is a relatively new but active research area in natural language processing that quantifies semantic similarities between linguistic elements considering the context in which they occur. Conceptualization algorithms on the other hand enriches short text such as words and phrases. This paper proposes an approach …that uses a map-reduce framework for combining these two techniques to generate conceptualized semantic clusters of phrases using distributional representation. Rigorous and systematic experiments on unstructured text datasets show that this approach can generate semantically rich and human interpretable concept clusters from large datasets. Further, the approach is scalable when dealing with high dimensional data since this method uses a map-reduce based framework for clustering. Show more

Keywords: Distributional semantics, concept extraction, semantic clustering, map-reduce, text mining

DOI: 10.3233/IDT-180089

Citation: Intelligent Decision Technologies, vol. 13, no. 2, pp. 153-160, 2019

Authors: Zeydan, Mithat | Kayserili, Sümeyra

Article Type: Research Article

Abstract: The solution of facility site selection problems is of utmost importance for the existence and future of the businesses. When planning for banking services, banks should ensure their accessibility standards by taking their customers and needs into account. For this reason, the accessibility of the services offered via ATM devices is very important. Contrary to the studies that have been widely done in the literature, in this study, the data mining techniques for determining locations of the ATMs, specifically decision tree algorithms were applied to select the best location among candidates considering new ATM installations. In practice, a data set …of a private bank’s ATM’s, which in the boundaries of the districts connected to downtown of Kayseri, was used. A decision support model based on rule-based location detection was created from the data set discussed. The correct classification scales were evaluated by applying different algorithms for decision tree method. Among the decision tree algorithms, model estimation accuracy was found to be 92.96% on average. The results were obtained using STATISTICA 10.0 Software. Show more

Keywords: Automatic Teller Machines (ATMs), decision tree algorithms, site selection

DOI: 10.3233/IDT-180084

Citation: Intelligent Decision Technologies, vol. 13, no. 2, pp. 161-175, 2019

Authors: Mandal, Kailash Pati | Mukherjee, Prasenjit | Chakraborty, Baisakhi | Chattopadhyay, Atanu

Article Type: Research Article

Abstract: Bengali is the seventh most widely spoken language in the world. Many researchers are working on developing Bengali language based information retrieval, question-answering, query-response systems. The proposed Bengali Language Query Processing System (BLQPS) is based on natural language query-response model. Bengali language has been used in the model to extract knowledge data from a default database. The system is based on scoring and pattern generation algorithm that is able to generate structure query language (SQL) from natural language query in Bengali with the help of a synonym database. The proposed system is domain based and a large number of words …have been initialized in the synonym database. The SQL is formulated from semantic analysis. Further, the generated SQL has been used to extract knowledge data in Bengali language from the default database. Show more

Keywords: Query-response, scoring and pattern generation based algorithm, Structure Query Language (SQL), Semantic analysis, Bengali Language Query Processing System (BLQPS), Natural Language Processing (NLP)

DOI: 10.3233/IDT-180074

Citation: Intelligent Decision Technologies, vol. 13, no. 2, pp. 177-192, 2019

Authors: Lassouaoui, Mourad | Boughaci, Dalila | Benhamou, Belaid

Article Type: Research Article

Abstract: Hyper-heuristics are high-level methods, used to solve various optimization problems. Some of them are capable of learning and adapting their behavior throughout the solving process. Selection hyper-heuristics evaluate low-level heuristics and determine which of them to be applied at a given point in the search process. However, it has been shown that the additive learning process becomes inefficient in hard problems where the probability of fitness improvement is less than 1 2 . Other alternative learning mechanisms have been proposed however they don’t take into account the synergy between the low-level heuristics. Moreover they haven’t been tested …on large NP-hard problems. In this work, we propose a new hyper-heuristic which we called the Multilevel Synergy Thompson Sampling Hyper-Heuristic . The proposed method includes both the probabilistic learning mechanism and the multilevel paradigm. The latter refers to the process of creating hierarchically smaller sub-problems from large problem instances. The proposed hyper-heuristic is applied on very large industrial Max-SAT instances from the latest Max-SAT competition. The numerical results are promising and demonstrate the benefits of our method. The proposed method outperforms the other four types of hyper-heuristics: Random, Choice Function, Stochastic Choice Function and the simple Thompson Sampling Hyper-Heuristics. Show more

Keywords: Selection hyper-heuristic, Max-SAT, Thompson sampling, choice-function, random, multilevel paradigm

DOI: 10.3233/IDT-180036

Citation: Intelligent Decision Technologies, vol. 13, no. 2, pp. 193-210, 2019

Authors: Kumar, Sumit | Tomar, Anurag Singh | Chaurasiya, Sandip K.

Article Type: Research Article

Abstract: Wireless Body Area Network (WBAN) has been emerged as a recent development in technology, which provides convenience to monitor patient health remotely. E-healthcare management has largely promoted by WBAN. It eases the life style of patient and refrain them from visiting hospital regularly. At present, E-healthcare is facing the issues of secure information transmission over public network and assistance to hospital staff for disease identification based on information collected from wearable sensors. In this paper, to preserve confidentiality of messages, knapsack cryptosystem based solution have proposed. To authenticate transmission of data in wireless body area network, we have proposed an …improved batch verification based digital signature scheme that improve the computational efficiency of underlying algorithm. Computing one-way hash for generation of digital signature is not required in proposed algorithm and hence provide a lightweight security mechanism suitable for resource constraint wearable sensors. A mechanism has proposed which facilitate the doctors to identify the disease probability by a probabilistic method of logistic regression. Further, a tree based batch validation approach has also proposed for vulnerable network. To validate the security of proposed scheme, various attacks have verified that the scheme has been capable to resist. Performance improvement in digital signature verification scheme has demonstrated and validated using implementation results. Show more

Keywords: Wireless body area network, logistic regression, prediction model, batch verification, knapsack crypto system, security, privacy and authentication, recovery based digital signature

DOI: 10.3233/IDT-170182

Citation: Intelligent Decision Technologies, vol. 13, no. 2, pp. 211-218, 2019

Authors: Srivastava, Awadhesh Kumar | Biswas, K.K.

Article Type: Research Article

Abstract: This paper proposes a method to human action recognition from RGB video clips. The method is based on capturing the local motion information from smaller size video clips. Local motion information is captured through accumulation of motion in different shape and size of patches of spatial domain. The motion information is then transformed to motion histograms. Further, all the histograms are concatenated to make the proposed feature vector. Bagging ensemble technique, in form of random forest, is used for classification. The idea is further extended to real time human action recognition mechanism. To show the robustness and efficiency of proposed …algorithm, it is performed on publicly available human action datasets Joint-annotated Human Motion Data Base (JHMDB) [29 ] and University of Rzeszów (UR) Fall detection dataset [19 ]. The results are also compared with other state of art methods. Show more

Keywords: Human action recognition, histogram, random forest, RGB camera, real time fall detection

DOI: 10.3233/IDT-170175

Citation: Intelligent Decision Technologies, vol. 13, no. 2, pp. 219-228, 2019

Authors: Zamini, Mohamad | Hasheminejad, Seyed Mohammad Hossein

Article Type: Research Article

Abstract: Anomaly detection is an important issue, which has been investigated in various research fields and application domains. Many anomaly detection techniques have been developed exclusively for certain application domains, in contrast, others are more general. This survey aims to create a structured and comprehensive overview of the research on anomaly detection. First, we tried to introduce the concept of anomalies and types of anomaly detection. We have tried to classify anomaly detection according to their application and then categorized their techniques. For each application and technique, we have described key assumptions, which are used by the techniques to distinguish between …normal and abnormal behavior. For each application, a basic anomaly detection technique has been provided, in the end; the differences among existing techniques in each specific category are discussed. Furthermore, we tried to describe the advantages and disadvantages of each technique in that field. In addition, we tried to bring some data sets that were used in some papers in order to test your methods with them. We hope that this survey provides a better concept of the various directions, which has been researched on that specific topic. Show more

Keywords: Anomaly detection, intrusion detection, fraud detection, credit card, banking industry

DOI: 10.3233/IDT-170155

Citation: Intelligent Decision Technologies, vol. 13, no. 2, pp. 229-270, 2019

Authors: Raval, R.M. | Prajapati, H.B. | Dabhi, V.K.

Article Type: Research Article

Abstract: In computer vision, recognizing human activity or behavior is a core challenging problem. This article provides a crisp study of human activity recognition systems in the area of visual surveillance. These systems are used for analysis and understanding of the human behavior. The study starts with the description of various emerging video processing domains, followed by a general process of human action recognition. Then the article covers human detection techniques from images and video. Finally, article also provides a survey of different features and models used in activity recognition systems and an overview of benchmark dataset of video surveillance. From …this state-of-the-art survey, researchers can outline promising directions of research. Show more

Keywords: Visual surveillance, Bag-of-Visual-Words, human activity recognition, motion features

DOI: 10.3233/IDT-170035

Citation: Intelligent Decision Technologies, vol. 13, no. 2, pp. 271-294, 2019