Journal of Intelligent & Fuzzy Systems - Volume 36, issue 5

Authors: Arunkumar, S. | Vairavasundaram, Subramaniyaswamy | Ravichandran, K.S. | Ravi, Logesh

Article Type: Research Article

Abstract: The development of the Internet of Things (IoT) can be attributed to the sudden rise in miniature electronic devices, as well as their computing power and ability to make interconnections. These devices exchange large volumes of confidential information from diverse locations. Similar to the Internet, the IoT has also encountered various issues with information security. Due to limited computing and energy resources in the field of IoT, it is necessary to develop a scheme to ensure feasible and more effective concealment and security properties. This paper proposes a unique methodology that captures an image using IoT sensors, which are subjected …to lighter cryptographic operations for conversion into a cipher image, and is then sent to a home server. At the home server, a combined cryptography and steganography approach is employed to conceal the cipher image in a cover image, camouflaging the presence of the secret image, which is then sent to the IoT-Cloud server for storage. During the embedding process, QR decomposition is performed on the RIWT transformed secret image and RIWT - DCT transformed cover image. Modification performed on the R matrix of QR decomposition does not affect the structural properties of the cover image. A block selection algorithm is used to select optimal blocks with high contrast areas to embed the secret image. The experimental results indicate that our scheme enhances imperceptibility, robustness, and resistance to steganalysis attacks. Show more

Keywords: Image steganography, RIWT, DCT, security in cloud, block selection algorithm, QR decomposition, IoT

DOI: 10.3233/JIFS-169984

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4265-4276, 2019

Authors: Chen, Lihui | Yang, Xiaomin | Lu, Lu | Liu, Kai | Jeon, Gwanggil | Wu, Wei

Article Type: Research Article

Abstract: The rapid developments of computation, communication and control contribute to the generation of cyber physical systems (CPS). For full-time urban surveillance or military reconnaissance in complex environments, infrared and visible imaging sensors typically need to be integrated into the CPS. Furthermore, an effective and stable image fusion algorithm is important for CPS to provide images with rich information. Therefore, an image fusion algorithm for CPS is introduced in this paper. Compared with traditional multi-scale and multi-direction decomposition based algorithms, a more efficient MSMD based algorithm is proposed. Firstly, base layers reserved edges and detailed layers are obtained by multi-scale decomposition. …Secondly, multi-direction decomposition is employed to base layers rather than detailed layers in traditional method. Then, serials of detailed layers and multi-directional base layers are obtained by choosing the max value based on patch. After the inverse transformation of multi-direction decomposition is conducted for multi-directional fused base layers, the reconstruction result is obtained via superposition of fused base and detail layers. Experiments prove that our algorithm outperforms the art-of-state. Show more

Keywords: Cyber-physical systems, image fusion, rolling guided filter, non-subsample directional filter bank

DOI: 10.3233/JIFS-169985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4277-4291, 2019

Authors: Narasappa, Jithendra Huchageeranahally | Rekha, D.

Article Type: Research Article

Abstract:  Wireless Mobile Ad hoc network is the foundation for the Vehicular Ad hoc Network which plays a pivotal role in disseminative safety information to the general public and can be used in commercial applications. Challenging issue in VANET is routing of data, due to swift mobility of vehicles and the network topology which changes with time and speed. Mobile Ad hoc Networks (MANETs) routing protocols cannot fully solve the unique characteristics in vehicular networks. Vehicular Ad hoc Networks system enables the intercommunication between the vehicles by allowing them exchange the traffic data or information. Such kind of exchange of information …may create privacy apprehension since the vehicle-generated information can contain much confidential information of the vehicle and its driver. Vehicular networks throw a plenty of unique challenges. The traffic information in the network may greatly affect the traffic decisions. Traffic View is a mechanism that can be routed with a vehicle as the future aspect. In this paper, Energy Aware Methodical Data Forwarding (EAMDF) Mechanism in Vehicular Ad hoc Networks is proposed, Information about the node is collected which is situated at the edge of radio range of the sender node (because of its proximity to the sender and as the information has to move in line with the destination route) and then the packet is transmitted by using the trustworthy greedy position based routing approach through that node. The key aspect of EAMDF mechanism is to prolong the energy of the nodes as well as increasing the packet delivery ratio. The results show that the through put is increased by 50%, packet delivery ratio is increased by 12.5% and also energy is prolonged in the network lifetime compared to other algorithms. Show more

Keywords: VANET, greedy forwarding, Energy Aware Methodical Data Forwarding (EAMDF)

DOI: 10.3233/JIFS-169986

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4293-4303, 2019

Authors: Banerjee, Anuradha | Akbar Hussain, D.M.

Article Type: Research Article

Abstract: Efficient servicing of requests in cloud environment has become need of the hour. Cloud services work based on zones in various locations and multiple service requests may be simultaneously considered as a batch and allocated to various zones. Experience-based Efficient Scheduling or EXES focuses on achieving minimum possible waiting time for a batch of requests, under the constraint that overall allocation cost should be less than or equal to a budget limit. Migration of tasks is also possible to balance loads if budget permits and we gain in energy. For each task in a batch and all available zones, a …priority value is computed based on previous interaction experience of the zone and the site that generated this task. The zone that produces highest priority for a task, is allocated the task. An SDN controller is in charge of the entire process of priority computation and assigning tasks to zones. Priority is given to requests generating from sites that consumed lesser execution time compared to other sites that have generated requests in request queue of the zone. To the best of authors’ knowledge, no existing scheduling scheme in cloud has considered batch processing based on service process experience of zones. Show more

Keywords: Allocation, budget, cloud, load, minimum waiting time, priority, software-defined-networks

DOI: 10.3233/JIFS-169987

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4305-4317, 2019

Authors: Kumar, C. | Sathish Kumar, K. | Indra Gandhi, V. | Vijayakumar, V. | Rawal, Bharat S

Article Type: Research Article

Abstract: This paper presents a new evolutionary approach for reconfiguration of radial systems. The framework applied for optimization is Symbiotic Organism Search Algorithm (SOSA). The algorithm is impressed by the interactive behavior opted by the living organisms for surviving and to propagate in the ecosystem. This concept aims for optimal survivability in the ecosystem involving the harm and benefits received from other organisms. The aim is to find optimal reconfiguration and to reduce the real power loss in the distribution side. This approach is examined on 16-bus and 33-bus systems. The results show a significant reduction of real power loss. The …time required for execution is less when compared to other approaches. Based on the results calculated with distribution load flow algorithm the SOSA gives better results in terms of real power loss reduction and it is best suitable for digital automation systems. Show more

Keywords: Symbiotic organism search algorithm, reconfiguration, power loss reduction

DOI: 10.3233/JIFS-169988

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4319-4326, 2019

Authors: Arivudainambi, D. | Dhanya, D.

Article Type: Research Article

Abstract:  Even though, cloud computing reduces the operating cost by enabling adaptation of virtual machines, it has suffered in selection of optimal virtual machine due to shortage of resource or resource wastage, sudden changes in requirement so it requires optimal resource allocation. Resource allocation is the process of providing services and storage space to the particular task requested by the users. This is one of the important challenges in cloud computing environment and has variant level of issues like scheduling task, computational performance, reallocation, response time and cost efficiency. In this research work we introduce a three-phase scheduling method based on …memory, energy and QOS in order to overcome the above issues which also yield low energy consumption, maximum storage and the high level Quality of Service (QoS). Biggest Memory First and Biggest Access First is introduced with NUMA scheduler and cache scheduler for memory scheduling and the optimal VM resulting from the three phases of scheduling is determined by Grey Wolf Optimization (GWO) algorithm. To carry the security level of optimized VMs, Streamline Security and Introspection security analysis are exhausted for detecting the malware VMs which results the secured and efficient VMs for further resource allocation. Our proposed methodology is implemented using the Cloud Sim tool and the experimental result shows the efficiency of our proposed method in terms of security, time consumption, and cost. Show more

Keywords: Quality of service (QoS), Biggest Access First (BAF), Biggest Memory First (BMF), Grey Wolf Optimization (GWO), streamline security, introspection security analysis

DOI: 10.3233/JIFS-169989

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4327-4340, 2019

Authors: Anusooya, G. | Vijayakumar, V. | Narayanan, V. Neela

Article Type: Research Article

Abstract:  Predicting the peak time load among data center and distributing the load will minimize the usage of the power consumption and also will minimize the carbon emission from data center. Reducing the carbon emission by lessening the energy consumption in a data center will impact on environment which will lead to a reduced carbon footprint. The proposed Water Shower Model (WSM) with Circular Peak Time Services (CPTS) has reduced the execution time to 10 ms comparing with Round Robin Algorithm. The load is shared among the data centers by predicting the type of request by the user as Read Only Request …(ROR) or Read Write Request (RWR). The ROR will assign the load to an optimized Container and the RWR will assign the load to a Virtual Machine. CPTS is a proposed model used to measure the carbon emission right from the idle state of the server in a datacenter and till it reaches the peak time of the load and vice versa. The advantage of existing Dynamic Voltage Frequency Scaling (DVFS) techniques is used in the proposed model to optimize the resource allotment and adjust the power and speed in computing devices which allocates only the required minimal amount of power for performing a task. Show more

Keywords: Green computing, load balancing, data center, carbon emission, container, virtual machine

DOI: 10.3233/JIFS-169990

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4341-4348, 2019

Authors: Devarajan, Malathi | Fatima, N. Sabiyath | Vairavasundaram, Subramaniyaswamy | Ravi, Logesh

Article Type: Research Article

Abstract:  Cyber-physical Social Network (CPSN) becomes an essential component of daily life. In recent years, CPSN has dragged millions of users to convey their social opinions. It is highly desirable to mine influential features from such diverse data to make a prediction on users’ Point of Interest (POI). Notably, social ties of the user in a specific location have a tendency to share similar opinions. Thus with the appearance of social links, the location based recommendations become popular to acquire reliable POI recommendations. Collaborative Filtering Recommender System (CFRS) able to discover reliable POI recommendations for the target user based on Location-based …CPSN. To enhance the performance of CFRS, a clustering ensemble model is proposed in this article. Four different swarm intelligent based cluster optimization algorithms were utilized to generate finite clusters. The experiment is conducted on two real-time social network dataset to exhibit the performance of the proposed CE-CFRS. The result shows that the clustering ensemble model outperforms a single clustering model in terms of assessment metrics. Show more

Keywords: Cyber-physical system, point-of-interest, location-based social network, collaborative filtering recommendation, clustering ensemble

DOI: 10.3233/JIFS-169991

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4349-4360, 2019

Authors: Raghav, R.S. | Dhavachelvan, P.

Article Type: Research Article

Abstract:  The Severe acute respiratory syndrome coronavirus (SARS-CoV) are deadly infectious disease which can easily transmit and causes severe problems in humans. It is known as a coronavirus and referred as a common form of virus that naturally causes upper-respiratory tract illnesses and the symptoms are hard to identify. It is important to recognize the patient and providing them with suitable action with constant intensive care. Healthcare amenities is constructed on fog and big-data based system and it is integrated with cyber-physical system. The role of Cyber physical system in health care domain is to fetch deep insights about the nature …of disease and carry the monitoring process with early detection of infected users. The objective is to identify occurrence of SARS at initial stage. In proposed system, resemblance factor is evaluated from the extracted keywords. In order to identify the difference between SARS affected and others, the proposed scheme fetches the inputs from user’s displayed in the form of text. It is passed to deep recurrent neural network (RNN) model. It extracts useful information from the raw information given by the user. The J48graft algorithm is used to carry the classification based on the type of infection and symptoms of each user. The data is stored in the bigdata layer (mongoDB) and it detects the infected area by using the geospatial feature in mongo dB. The methodology is framed in the proposed model to prevent the spread of disease to other users. In case of any abnormality the generation of alert process is done instantaneously and directed on user’s mobile from fog layer. The final experimental outcome reveals information about the performance of proposed system in terms of Success rate, failure rate, latency and accuracy %. It shows that the proposed algorithm gives high level of accuracy when it is compared with other primitive methods. Show more

Keywords: Cyber physical systems, deep recurrent neural network, J48graft, bigdata analytics, fog layer, mongo dB

DOI: 10.3233/JIFS-169992

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4361-4373, 2019

Authors: Zhu, Xuhui | Ni, Zhiwei | Ni, Liping | Jin, Feifei | Cheng, Meiying | Li, Jingming

Article Type: Research Article

Abstract:  Ensemble pruning is usually used to improve classification ability of an ensemble using less number of classifiers, and it is an NP-hard problem. Existing ensemble pruning approaches always find the optimal sub-ensemble using diversity of classifiers or running heuristic search algorithms separately. Diversity and accuracy of classifiers are widely recognized as two important properties of an ensemble. The increase of the diversity of classifiers must lead to the decrease of the average accuracy of the whole classifiers, and vice versa, so there is a tradeoff between diversity and accuracy of classifiers. Finding the tradeoff is the key to a successful …ensemble. Heuristic algorithms have good results when it comes to finding the tradeoff, but it is unfeasible to do an exhaustive search. Hence, we propose a Spread Binary Artificial Fish swarm algorithm combined with a Double-fault measure for Ensemble Pruning (SBAFDEP) using a combination of diversity measures and heuristic algorithms. First, the classifiers in an initial pool are pre-pruned using a double-fault measure, which significantly alleviates the computational complexity of ensemble pruning. Second, the final ensemble is efficiently assembled from the retaining classifiers after pre-pruning using the proposed Spread Binary Artificial Fish Swarm Algorithm (SBAFSA). Simulation and experiment results on 25 UCI datasets show that SBAFDEP performs better than other state-of-the-art pruning approaches. It provides a novel research idea for ensemble pruning. Show more

Keywords: Artificial fish swarm algorithm, spread behavior, double-fault measure, diversity, ensemble pruning

DOI: 10.3233/JIFS-169993

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4375-4387, 2019

Authors: Khan, Munna | Reza, Md Qaiser | Salhan, Ashok Kumar | Sirdeshmukh, Shaila P.S.M.A.

Article Type: Research Article

Abstract:  The acoustic resonance spectroscopy is an accurate, precise, inexpensive, and non-destructive method for identification and quantification of materials. The acoustics based inspection methods used for classification of materials in the field of food, security, and healthcare is constrained by expensive instrumentation, complicated transducer coupling, etc. Hence, a simple, inexpensive, and portable system has been devised that acquires data quickly and classifies the materials. It has two piezoelectric transducers glued to both ends of the V-shaped quartz tube, one acting as a transmitter and another as a receiver. The transmitter generates vibration by white noise excitation. The receiver detects the resultant …signal after interaction with samples and recorded the acoustic signal with the help of a laptop and software. From analysis of power spectrum of signals acquired from each of the samples, seven resonant peaks were obtained. PCA analysis was carried out by selecting only two principal components as feature vectors for classification. The overall accuracy of the classifiers: LDA and Naive Bayes were 98.91% and 96.83% respectively. The classification accuracy of LDA for distilled water, sugar solution, and salt solution were found to be 100%, 98.5%, and 98.25% respectively, while the accuracy of the Naive Bayes classifier was 94%, 98.5%, and 98% respectively. The results show that the classification accuracy of LDA is better than Naive Bayes classifier. The datasets of the developed simple system show a significant capability in the classification of materials. Show more

Keywords: Acoustic resonance spectroscopy (ARS), acoustic signature, principal component analysis (PCA), linear discriminant analysis (LDA)

DOI: 10.3233/JIFS-169994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4389-4397, 2019

Authors: Chhabra, Rishu | Krishna, C. Rama | Verma, Seema

Article Type: Research Article

Abstract:  Intelligent Transportation Systems (ITS) aim at reducing the risks associated with the transportation system as road accidents are becoming one of the primary causes of death in developing countries. Monitoring of driver behavior is one of the key areas of ITS and assists in vehicle safety systems. It has gained importance in order to reduce traffic accidents and ensure the safety of all the road users, from the drivers to the pedestrians. In this work, we present a context-aware system that considers the vehicle, driver and the environment for driver behavior classification as a safe or fatigue or unsafe driver …(representing any other unsafe driving behavior like a drunk driver, reckless driver etc.) using a Dynamic Bayesian Network (DBN). We have designed a questionnaire to obtain the influencing factors that decide safe, unsafe and fatigue driving behavior. The collected data has been analyzed using Statistical Package for Social Sciences (SPSS). It has been observed that several techniques in the past have been proposed for driver behavior classification or detection; which either use specialized sensors or hardware devices, inbuilt smartphone sensors (like a gyroscope, accelerometer, magnetometer and GPS etc.), complex sensor fusion algorithms and techniques to detect driver behavior. The novelty of our work lies in designing and developing a context-aware system based on Android smartphone; that considers the complete driving context (driver, vehicle and surrounding environment) and classifies the driver behavior using a DBN. In order to identify driver fatigue, results from the designed questionnaire and previous research studies have been used without the need for special hardware devices. A DBN that combines all the contextual information has been created using GeNIe Modeler. Learning of DBN has been carried out using the Expec-tation–Maximization (EM) algorithm. The real-time data for DBN learning and testing has been collected on Chandigarh-Patiala National Highway, India using an Android smartphone. The proposed system yields an overall classification accuracy of 80–83%.The focus of this paper is to develop a cost-effective context-aware driver behavior classification system, to promote ITS in developing countries. Show more

Keywords: DBN, driving behavior, intelligent transportation systems, sensors, smartphone

DOI: 10.3233/JIFS-169995

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4399-4412, 2019

Authors: Pradhan, Buddhadeb | Vijayakumar, V. | Hui, Nirmal Baran | Sinha Roy, Diptendu

Article Type: Research Article

Abstract:  Navigation of multiple robots is a challenging task, particularly for many robots, since individual gains may more often than not adversely affect global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace without colliding amongst themselves. Two solutions for coordination namely Fuzzy Logic Controller (FLC) and Genetic Algorithm based FLC (GA-FLC) have been employed and the efficacy of cooperation strategies have been compared with their non-cooperative counterparts as well as with the fundamental potential field method (PFM). Proposed coordination schemes are verified through simulations. A total of 100 scenarios are considered varying …the number of robots (8, 12, 16 and 20). The obtained results show the efficacy of the proposed schemes. Show more

Keywords: Multi-agent systems, motion planning, coordination

DOI: 10.3233/JIFS-169996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4413-4423, 2019

Authors: Algredo-Badillo, Ignacio | Morales-Rosales, Luis Alberto | Hernandez-Gracidas, Carlos Arturo | Cruz-Victoria, Juan Crescenciano | Pacheco-Bautista, Daniel | Morales-Sandoval, Miguel

Article Type: Research Article

Abstract:  Object detection is a technologically challenging issue, which is useful for safety in outdoor environments, where this object, frequently, represents an obstacle that must be avoided. Although several object detection methods have been developed in recent years, they usually tend to produce poor results in outdoor environments, being mainly affected by sunlight, light intensity, shadows, and limited computational resources. This open problem is the main motivation for exploring the challenge of developing low-cost object detection solutions, with the characteristic of being easily adaptable and having low power requirements, such as the ones needed in on-board obstacle detection systems in automobiles. …In this work, we present a trade-off analysis of several architectures using an FPGA-based design that implements ANNs (FPGA-ANN) for outdoor obstacle detection, focused in road safety. The analyzed FPGA-ANN architectures merge outdoor data gathered by a Kinect sensor, images and infrared data, to construct an outdoor environment model for object detection, which allows to detect if there is an obstacle in the near surroundings of a vehicle. Show more

Keywords: Obstacle detection, artificial neural networks, FPGA implementation, architecture trade-off analysis, road safety

DOI: 10.3233/JIFS-169997

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4425-4436, 2019

Authors: Sinha, Rupesh Kumar | Sahu, S.S.

Article Type: Research Article

Abstract:  Cryptography is the most peculiar way to secure data and most of the encryption algorithms are mainly used for textual data and not suitable for transmission data such as images. It is seen that the generation of secure key in Image cryptography has been a challenging task in the way of providing secured key generation for the transmitted data. In order to aid secured key generation in this context, an optimized secret key generation based on Chebyshev polynomial with Adaptive Firefly (FF) optimization technique is proposed. The optimized key is utilized with process of shuffling, diffusion, and swapping to get …a better encrypted image. At the receiver end, reverse process is applied with optimized key to retrieve the original input image. The efficiency of our proposed method is assessed by the exhaustive experimental study. The results show that the proposed methodology provided correlation coefficient of 0.21, Number of Pixels Change Rate (NPCR) of 0.996, Unified Average Changing Intensity (UACI) of 0.3346 and Information Entropy of 7.995 as compared with the existing methods. Show more

Keywords: Encrypted image, DWT, Chebyshev polynomial, optimized secret key, Adaptive firefly (FF) optimization algorithm

DOI: 10.3233/JIFS-169998

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4437-4447, 2019

Authors: Srinivasan, Sundar | ShivaKumar, K.B. | Muazzam, Mohammad

Article Type: Research Article

Abstract: A cognitive radio (CR) can be programmed and configured dynamically to use best wireless channels. Such a radio automatically detects available channels in wireless spectrum, and then accordingly changes its transmission. The CR system consists of primary user or licensed user and secondary user or unlicensed user. The security attacks such as active attack and passive attack are identified between primary user and secondary user and packet loss occurs during packet transmission. The security problem occurring while transmission of signal between primary user and secondary user is rectified by using a hybrid RSA (Riverest, Shaimer and Adleman) and HMAC (Hash …Message Authentication Code) algorithms where former is used for key generation and latter is used for tag generation which is sent along with signal. Additionally packet loss incurred in system incurs is reduced with aid of Markov Chain Model during transmission. The comparison results provided showefficiency of the proposed algorithm in cognitive radio system in terms of parameters such as throughput, encryption time, decryption time, Packet Delivery Ratio and energy consumption. Show more

Keywords: Cognitive radio, RSA (Riverest, Shaimer and Adleman), HMAC (Hash Message Authentication Code), Markov Chain Model, active attack, passive attack

DOI: 10.3233/JIFS-169999

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4449-4459, 2019

Authors: Vijayakumar, K. | Rajesh, K. | Vishnuvardhanan, G. | Kannan, S.

Article Type: Research Article

Abstract:  The Distributed Generation (DG) systems are highly useful in recent days for increasing the penetration of renewable energy, in which the design of grid connected inverters is one of the demanding and challenging task. For this reason, different controller strategies are developed in the traditional works for controlling the inverters with increased efficiency. But, it has the major limitations of increased computational complexity, steady state error and reduced compensation capability. To solve these issues, this research work aims to design a new controller by implementing a novel Monkey King Evolution Algorithm (MKEA) for grid connected converters. The motive of this …work is to increase the overall effectiveness of the power system by controlling the inverter without affecting its output. Also, it aims to provide a secure and convenient controller for the power converters. Here, the information that is obtained from the system which includes real power, distorted power due to load, reactive power of load, and apparent power of inverter are taken as the input. Later, the four numbers of monkeys are initialized, which evaluates the best solution based on these parameters. Sequentially, the monkey king obtains the best solutions from the monkeys, using which the most suitable and best solution for taking the decision is selected. Based on this, the reference current is generated by performing the voltage regulation, and abc to dq0 transformation processes. During simulation, the efficiency of the controller is analyzed by using the measures of phase voltage, phase current, active power, reactive power, apparent power, grid voltage, and output voltage. The Total Harmonic Distortion (THD) is effectively reduced by using the MKEA based controller design. Extensive simulation and experimental results are presented to validate the effectiveness of the proposed controller and control strategy. Show more

Keywords: Grid connected inverters, Distributed Generation System, Monkey King Evolution Algorithm (MKEA), Photovoltaic (PV) System, controller design, reference current generation

DOI: 10.3233/JIFS-179000

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4461-4478, 2019

Authors: Jain, Parul | Dixit, Veer Sain

Article Type: Research Article

Abstract:  Context aware recommender system has become an area of rigorous research attributing to incorporate context features, thereby increases accuracy while making recommendations. Most of the researches have proved neighborhood based collaborative filtering to be one of the most efficient mechanisms in recommender systems because of its simplicity, intuitiveness and wide usage in commercial domains. However, the basic challenges observed in this area include sparsity of data, scalability and utilization of contexts effectively. In this study, a novel framework is proposed to generate recommendations independently of the count and type of context dimensions, hence pertinent for real life recommender systems. In …the framework, we have used k -prototype clustering technique to group contextually similar users to get a reduced and effective set. Additionally, particle swarm optimization technique is applied on the closest cluster to find the contribution of different context features to control data sparsity problem. Also, the proposed framework employs an improved similarity measure which considers contextual condition of the user. The results came from the series of experiments using two context enriched datasets showcasing that the proposed framework increases the accuracy of recommendations over other techniques from the same domain without consuming extra cost in terms of time. Show more

Keywords: Collaborative filtering, unsupervised learning, particle swarm optimization, euclidean distance, context aware recommendations

DOI: 10.3233/JIFS-179001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4479-4490, 2019

Authors: Hasnat, Abul | Barman, Dibyendu | Sarkar, Suchintya

Article Type: Research Article

Abstract:  Shared visual cryptography is a method to protect image-based secrets where an image is kept as multiple shares having less computational decoding process. Steganography is a technique to hide secret data in some carrier like-audio, image etc. Steganography technique is categorized into four categories. i) Spatial Domain Technique- Image pixel values are converted into binary and some of the binary values changed to hide secret data. ii) Transform Domain Technique- the message is hidden in cover image and then it is transformed in the frequency domain. iii) Distortion Technique-information is stored by changing the value of the pixel. iv)Visual Cryptography …Technique-Image is broken into two or more parts called shares. This article proposes a hybrid visual crypto-steganography approach which exploits the advantages of both approaches to protect image based secret in communication. Most of the visual cryptography is applied on black and white images but the proposed method can be applied directly on color images having three channels. This method does not change the image size. Also an exact replica of original image can be reconstructed therefore this process does not result in image quality degradation. This article proposes novel color image share cryptography where seven shares are generated from one color image (correlated/de-correlated color space). These shares are sent to the receiver and original image is reconstructed using all those shares. Share generation and image reconstruction is based on simple operation like pixel shuffling, reversing binary string of the image information, ratio of pixel intensity values. Row key matrix and column key matrix are generated using random function. Pixel positions are shuffled using these two key matrixes. These seven shares namely Row Key, Column Key, Remainder matrix, Quotient matrix, R ratio matrix, G ratio matrix and B ratio matrix are generated. Then Row key matrix, Column key matrix, Remainder matrix, Quotient matrix and three ratio matrices are hidden into separate cover images by LSB encoding technique and sent over the network. Receiver can reconstruct the image if all shares are available only. The proposed method is applied on standard images in the literature and images captured using standard digital camera. Comparison study with existing methods shows that the proposed method performs better in terms of NIST metrics. The method has many applications in the area of visual cryptography, shared cryptography, image based authentication etc. Show more

Keywords: Binary image, Cryptography, GCD, image decryption, image encryption, image security, quotient, remainder, shared visual cryptography, steganography

DOI: 10.3233/JIFS-179002

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4491-4506, 2019

Authors: Ramachandran, Sumalatha | Palivela, Lakshmi Harika

Article Type: Research Article

Abstract:  The importance of the surveillance is increasing every day. Surveillance is monitoring of activities, behavior and other changing information. An intelligent automatic system to detect behavior of the human is very important in public places. For this necessity, a framework is proposed to detect suspicious human behavior as well as tracking of human who is doing some unusual activity such as fighting and threatening actions and also distinguishing the human normal activities from the suspicious behavior. The human activity is recognized by extracting the features using the convolution neural network (CNN) on the extracted optical flow slices and pre-training the …activities based on the real-time activities. The obtained learned feature creates a score for each input which is used to predict the type of activity and it is classified using multi-class support vector machine (MSVM). This improved design will provide better surveillance system than existing. Such system can be used in public places like shopping mall, railway station or in a closed environment such as ATM where security is the prime concern. The performance of the system is evaluated, by using different standard datasets having different objects and achieved 95% performance as explained in experimental analysis. Show more

Keywords: Suspicious activity detection, optical flow, convolutional neural networks, support vector machine, multi-class SVM

DOI: 10.3233/JIFS-179003

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4507-4518, 2019

Authors: Jangiti, Saikishor | Sri Ram, E. | Ravi, Logesh | Sriram, V.S. Shankar

Article Type: Research Article

Abstract:  With the advent of cloud computing, a cost-effective and reliable choice to employ IT infrastructure, the cyber-physical systems (CPS) are transforming into loosely coupled cloud and fog CPS. The sensor information from physical processes at CPS is continuously processed by fog computing nodes and is forwarded for advanced data analytics offered as a service from the cloud. The computation offloaded by fog devices are initiated as Virtual Machines (VMs) in the cloud data center. The effective placement of these VMs into minimum Physical Machines (PMs) involves economic and environmental issues. Recent research works signify the use of First-Fit Decreasing (FFD) …based heuristic techniques to address this NP-Hard problem as a vector bin-packing problem. In this research work, we present a set of hybrid heuristics and an ensemble heuristic to improve the solution quality. The simulation results show that the proposed heuristics are highly scalable and economical in comparison with the individual heuristic-based approaches. Show more

Keywords: cyber-physical systems, fog computing, cloud computing, virtual machine placement, first-fit decreasing

DOI: 10.3233/JIFS-179004

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4519-4529, 2019

Authors: Chalapathi, G.S.S. | Chamola, Vinay | Gurunarayanan, S.

Article Type: Research Article

Abstract:  Wireless Sensor Networks (WSNs) are set to play an important role in the Internet of Things (IoT). WSNs are deployed for many IoT applications like Smart-Street Lighting, Smart-Grid, etc. Time Synchronization Protocol (TSP) is an important protocol in WSNs and it is used for many of its operations. Most of the existing TSPs for WSNs are simulation-based works, which do not fully prove their effectiveness for WSNs. Further, the Line-of-Sight (LOS) conditions in which the WSN nodes are deployed can significantly affect the performance of these TSPs. However, most of the existing protocols neither talk about the LOS conditions in …which these protocols were tested nor prove their effectiveness for different LOS conditions. To address these aspects, a synchronization protocol for cluster-based WSNs called a Simple Hierarchical Algorithm for Time Synchronization (H-SATS) has been proposed in this work and its performance is tested on a densely deployed large-sized WSN testbed in different LOS conditions. Further, H-SATS has been compared with the traditional regression-based method, which is the core synchronization scheme for different synchronization protocols in clustered WSNs. Experiments show that H-SATS outperforms the regression method in terms of synchronization accuracy to a maximum of 26.7% for a 30-node network. Show more

Keywords: Cluster-based topology WSN, line-of-sight (LOS) conditions, non-line-of-sight (NLOS) condition, time synchronization protocol, wireless sensor networks (WSN)

DOI: 10.3233/JIFS-179005

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4531-4543, 2019

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4545-4545, 2019

Authors: Pinto, D. | Singh, V.

Article Type: Editorial

DOI: 10.3233/JIFS-179006

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4547-4552, 2019

Authors: Solovyev, Valery | Solnyshkina, Marina | Ivanov, Vladimir | Batyrshin, Ildar

Article Type: Research Article

Abstract: Education policy makers view measuring academic texts readability and profiling classroom textbooks as a primary task of education management aimed at sustaining quality of reading programs. As Russian readability metrics, i.e. “objective” features of texts determining its complexity for readers, are still a research niche, we undertook a comparative analysis of academic texts features exemplified in textbooks on Social Science and examination texts of Russian as a foreign language. Experiments for 7 classifiers and 4 methods of linear regression on Russian Readability corpus demonstrated that ranking textbooks for native speakers is a much more difficult task than ranking examination texts …written (or designed) for foreign students. The authors see a possible reason for this in differences between two processes: acquiring a native language on the one hand and learning a foreign language on the other. The results of the current study are extremely relevant in modern Russia which is joining the Bologna Process and needs to provide profiled texts for all types of learners and testees. Based on a qualitative and quantitative analysis of a text, the research offers a guide for education managers to help build consensus on selecting a reading material when educators have differing views. Show more

Keywords: Text readability, machine learning, Russian academic text, text complexity, examination tests

DOI: 10.3233/JIFS-179007

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4553-4563, 2019

Authors: Garcia-Gorrostieta, Jesús Miguel | López-López, Aurelio

Article Type: Research Article

Abstract:  Academic writing is a complex task which requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill difficult to master. Aiming to contribute to develop this skill, we present a freely available annotated corpus to support research in argumentation in Spanish. To build it, we elaborated an annotation guide to identify argumentation in paragraphs. The guide also specified how to determine segments of sentences as a claim or premise, and to …indicate relations (support or attack) between such segments. Then, an annotated corpus of 300 sections was created. After its construction, the corpus was used to perform an exploratory analysis which aimed to identify and present the amount of argumentation in each section, as well as resulting patterns for argument identification. Hence, we also report an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. The results of the experiments to evaluate argumentative paragraph detection were encouraging. In addition, we discuss a web-based prototype for argument detection in paragraphs to reach the broader academic community of students, instructors and researchers. Show more

Keywords: Argumentation, academic writing, annotated theses corpus, argumentative paragraph detection, argument markers

DOI: 10.3233/JIFS-179008

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4565-4577, 2019

Authors: Sánchez, Belém Priego | Pinto, David

Article Type: Research Article

Abstract: In this paper we present an unsupervised technique for validating the existence of verbal phraseological units in raw text. This technique employs the concept of internal and contextual attraction which basically considers a mathematical formula based on co-occurrence of terms inside and outside of the terms considered to be part of a verbal phraseological unit. The experiments carried out using a corpus of news stories report a 60% of accuracy, which highlights the challenging task of automatic validation of verbal phraseological units in raw texts.

Keywords: Unsupervised methods, term co-occurrence, phraseological units

DOI: 10.3233/JIFS-179009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4579-4585, 2019

Authors: Reyes-Magaña, Jorge | Bel-Enguix, Gemma | Gómez-Adorno, Helena | Sierra, Gerardo

Article Type: Research Article

Abstract: This work introduces a lexical search model based on a type of knowledge graphs, namely word association norms. The aim of the search is to retrieve a target word, given the description of a concept, i.e., the query. This differs from traditional information retrieval models were complete documents related to the query are retrieved. Our algorithm looks for the keywords of the definition in a graph, built over a corpus of word association norms for Mexican Spanish, and computes the centrality in order to find the relevant concept. We performed experiments over a corpus of human-definitions in order to evaluate …our model. The results are compared with a Boolean information retrieval (IR) model, the BM25 text-retrieval algorithm, an algorithm based on word vectors and an online onomasiological dictionary–OneLook Reverse Dictionary. The experiments show that our lexical search method outperforms the IR models in our study case. Show more

Keywords: Information retrieval, word association norms, natural language graphs, lexical search

DOI: 10.3233/JIFS-179010

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4587-4597, 2019

Authors: González, José-Ángel | Segarra, Encarna | García-Granada, Fernando | Sanchis, Emilio | Hurtado, Llu’ıs-F.

Article Type: Research Article

Abstract: In this paper, we present an extractive approach to document summarization based on Siamese Neural Networks. Specifically, we propose the use of Hierarchical Attention Networks to select the most relevant sentences of a text to make its summary. We train Siamese Neural Networks using document-summary pairs to determine whether the summary is appropriated for the document or not. By means of a sentence-level attention mechanism the most relevant sentences in the document can be identified. Hence, once the network is trained, it can be used to generate extractive summaries. The experimentation carried out using the CNN/DailyMail summarization corpus shows the …adequacy of the proposal. In summary, we propose a novel end-to-end neural network to address extractive summarization as a binary classification problem which obtains promising results in-line with the state-of-the-art on the CNN/DailyMail corpus. Show more

Keywords: Siamese neural networks, hierarchical attention networks, automatic text summarization

DOI: 10.3233/JIFS-179011

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4599-4607, 2019

Authors: Millán-Hernández, Christian Eduardo | García-Hernández, René Arnulfo | Ledeneva, Yulia

Article Type: Research Article

Abstract:  Confused drug names are a common cause of medication errors, and are related to look-alike and sound-alike drug names. For the problem of identifying confused drug name pairs, individual similarity measures are used between the drug names. In the state-of-art, a logistic regression with the standard learning algorithm has been used to combine individual similarity measures. However, only three similarity measures have been combined but the results of previous research do not outperform with a statistical significance to any individual measure. In addition, the problem of potential confused drug names pairs presents a high unbalanced distribution of dataset that it …is a hard problem to supervised machine learning models. In this paper, an improved combined logistic regression measure based on 21 individual measures is presented with the standard learning algorithm. Also, we present an evolutionary learning method for a combined logistic regression measure that allows to learn an unbalanced dataset. According to the experimentation with a gold standard dataset, our proposed combined measures outperform previous research with a statistical significance to identify pairs of confused drug names. In addition, the rankings of individual and combined similarity measures are presented. Show more

Keywords: Look-alike sound-alike drug names, patient safety, logistic regression, genetic algorithm, imbalanced dataset.

DOI: 10.3233/JIFS-179012

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4609-4619, 2019

Authors: García-Calderón, Miguel Ángel | García-Hernández, René Arnulfo | Ledeneva, Yulia

Article Type: Research Article

Abstract: Text Line Segmentation (TLS) methods are intended to locate and separate text lines in document images for different stages of image analysis such as word spotting, keyword search, text alignment, text recognition and other stages of indexation involved in the retrieval of information from handwritten documents. The design of the proposed methods for the TLS and the tuning of their parameters assume a level of complexity according to the language and the writing style of a document collection. Therefore, the performance of these methods is not maintained against documents of greater or lesser complexity. In this paper, we present TLS-ICI, …a TLS Intrinsic Complexity Index that allows measuring the complexity of a document for the TLS task, without the necessity of a human gold standard. Through experimentation, we demonstrate how our proposed TLS-ICI provides an order to both the TLS methods and the image-based handwritten documents. In this way, with our proposed complexity index it is possible to select the most appropriated method for each document of a collection, reducing the time spent in exhaustive tests and increasing the performance. In addition, we demonstrate through a new hybrid TLS method that the TLS-ICI outperforms previous individual TLS methods. The dataset consists of several standard TLS collections of contemporary and ancient texts from different languages and alphabets such as English, Spanish, Arabic, and Chinese, Greek, Khmer, Persian, Bengali, Oriya, Kannada and Nahuatl. Show more

Keywords: Visual complexity in handwritten documents, handwritten text line segmentation, text line segmentation, document image processing, projection profile, historical documents, multilingual document analysis, handwritten recognition

DOI: 10.3233/JIFS-179013

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4621-4631, 2019

Authors: Fócil-Arias, Carolina | Sidorov, Grigori | Gelbukh, Alexander

Article Type: Research Article

Abstract: The rapid growth in the extraction of clinical events from unstructured clinical records has raised considerable challenges. In this paper, we propose the use of different features with a statical modeling method called conditional random fields, which is consider an algorithm for effectively solving problems of sequence tagging. Our goal is to determine which feature selection can affect the performance of four subtasks presented in SemEval Task-12: Clinical TempEval 2016. We applied a careful preprocessing, where the proposed method was tested on real clinical records from Task-12: Clinical TempEval 2016. The comparative analyses obtained indicate that our proposal achieves good …results compared to the work presented in Task-12: Clinical TempEval 2016 challenges. Show more

Keywords: Clinical reports, medical information extraction, natural language processing, machine learning, feature selection, conditional random fields

DOI: 10.3233/JIFS-179014

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4633-4643, 2019

Authors: Brena, Ramon | Ramirez, Eduardo

Article Type: Research Article

Abstract:  Detection of topics in Natural Language text collections is an important step towards flexible automated text handling, for tasks like text translation, summarization, etc. In the current dominant paradigm to topic modeling, topics are represented as probability distributions of terms. Although such models are theoretically sound, their high computational complexity makes them difficult to use in very large scale collections. In this work we propose an alternative topic modeling paradigm based on a simpler representation of topics as overlapping clusters of semantically similar documents, that is able to take advantage of highly-scalable clustering algorithms. Our Query-based Topic Modeling framework (QTM) …is an information-theoretic method that assumes the existence of a “golden” set of queries that can capture most of the semantic information of the collection and produce models with maximum “semantic coherence”. QTM was designed with scalability in mind and was executed in parallel using a Map-Reduce implementation; further, we show complexity measures that support our scalability claims. Our experiments show that the QTM can produce models of comparable or even superior quality than those produced by state of the art probabilistic methods. Show more

Keywords: Topics NLP clustering queries

DOI: 10.3233/JIFS-179015

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4645-4657, 2019

Authors: Gupta, Vedika | Singh, Vivek Kumar | Ghose, Udayan | Mukhija, Pankaj

Article Type: Research Article

Abstract: This paper tries to map the research work carried out in the field of Big Data through a detailed analysis of scholarly articles published on the theme during 2010-16, as indexed in Scopus. We have collected and analyzed all relevant publications on Big Data, as indexed in Scopus, through a quantitative as well as textual characterization. The analysis attempts to dwell into parameters like research productivity, growth of research and citations, thematic trends, top publication sources and emerging topics in this field. The analytical study also investigates country-wise publications output and impact in terms of average citations per paper, country-level …collaboration patterns, authorship and leading contributors (countries, institutions) etc. The scholarly publication data is also subjected to a detailed textual analysis method to identify key themes in Big Data research, disciplinary variations and thematic trends and patterns. The results produce interesting inferences. Quantitative measures show that there has been a tremendous increase in number of publications related to Big Data during last few years. Research work in Big Data, though primarily considered a sub-discipline of Computer Science, is now carried out by researchers in many disciplines. Thematic analysis of publications in Big Data show that it’s a discipline involving research interest from fields as diverse as Medicine to Social Sciences. The paper also identifies major keywords now associated with Big Data research such as Cloud Computing, Deep Learning, Social Media and Data Analytics. This helps in a thorough understanding and visualization of the Big Data research area. Show more

Keywords: Big data, big data analytics, data science, scientometrics

DOI: 10.3233/JIFS-179016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4659-4675, 2019

Authors: Figueroa, Karina | Camarena-Ibarrola, Antonio | Valero-Elizondo, Luis | Reyes, Nora

Article Type: Research Article

Abstract:  Similarity searching is the core of many applications in artificial intelligence since it solves problems like nearest neighbor searching. A common approach to similarity searching consists in mapping the database to a metric space in order to build an index that allows for fast searching. One of the most powerful searching algorithms for high dimensional data is known as the permutation based algorithm (PBA) . However, PBA has to collect the most similar permutations to a given query’s permutation. In this paper, how to speed up this process by proposing several novel hash functions for Locality Sensitive Hashing (LSH) …with PBA is shown. As a matter of fact, at searching our technique allows discarding up to 50% of the database to answer the query with a candidate list obtained in constant time. Show more

Keywords: Nearest neighbor, similarity searching, metric spaces

DOI: 10.3233/JIFS-179017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4677-4684, 2019

Authors: Pathak, Amarnath | Pakray, Partha | Gelbukh, Alexander

Article Type: Research Article

Abstract: Scientific documents, which are majorly constituted of math formulae, form a primary source of scientific and technical information. However, the indexing and the search processes of conventional search engines barely account for mathematical contents of such documents. Though the recent past has witnessed a surge in number of Mathematical Information Retrieval (MIR) systems intending to retrieve math formulae from scientific documents, the low values of their evaluation measures are indicative of the scope for improvement. To cope with the challenges of MIR, and to further the performance of state-of-the-art systems, a novel approach, called Binary Vector Transformation of Math Formula …(BVTMF), is introduced. The implemented system extracts MathML formulae from the documents, preprocesses them, and renders them into fairly large-sized binary vectors (vectors of ‘0’s and ‘1’s). Generated formula vector is representative of the information content of corresponding formula. For indexing and searching text contents, the system relies on Apache Lucene. Text and math search results retrieved by independent text and math sub-systems are re-ranked to prioritize the results containing text as well as math components of the user query. Quality of the retrieved search results and appreciable values of the evaluation measures substantiate competence of the proposed approach. Show more

Keywords: Mathematical information retrieval, binary vector transformation, math formula search, scientific document retrieval, precision, bit position information table

DOI: 10.3233/JIFS-179018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4685-4695, 2019

Authors: Hurtado, Lluís-F. | González, José-Ángel | Pla, Ferran

Article Type: Research Article

Abstract: Natural Language Processing problems has recently been benefited for the advances in Deep Learning. Many of these problems can be addressed as a multi-label classification problem. Usually, the metrics used to evaluate classification models are different from the loss functions used in the learning process. In this paper, we present a strategy to incorporate evaluation metrics in the learning process in order to increase the performance of the classifier according to the measure we are interested to favor. Concretely, we propose soft versions of the Accuracy, micro-F 1 , and macro-F 1 measures that can be used as loss …functions in the back-propagation algorithm. In order to experimentally validate our approach, we tested our system in an Emotion Classification task proposed at the International Workshop on Semantic Evaluation, SemEval-2018. Using a Convolutional Neural Network trained with the proposed loss functions we obtained significant improvements both for the English and the Spanish corpora. Show more

Keywords: Deep Learning, loss function, multi-label classification, Natural Language Processing, Emotion Classification

DOI: 10.3233/JIFS-179019

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4697-4708, 2019

Authors: Rodríguez, Fernando M. | Garza, Sara E.

Article Type: Research Article

Abstract: Emotions, which are now commonly portrayed in social media, play a fundamental role in decision making. Having this into account, this work proposes a model to predict (forecast) emotions in social networks. This model specifically predicts, for a user, the proportion of comments that will be published with a particular emotion; this proportion is defined as an emotional intensity of the user in a particular time period. On the contrary of other models, which are focused on a single emotion, the proposed model considers a basic scheme of four emotions and employs these in an interdependent manner. The model, …moreover, utilizes three types of features: (1) user-related, (2) contact-related, and (3) environment-related. Prediction is performed using linear regression. Nearly 20 models, including ARIMA, are outperformed by the proposed model (with statistically significant results) when evaluated over a dataset extracted from Twitter. Some potential applications include massive opinion monitoring and recommendations to improve the emotional wellness of social media users (for example, the recommendation of joyful memories). Show more

Keywords: Prediction, emotion, machine learning, Twitter, social networks

DOI: 10.3233/JIFS-179020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4709-4719, 2019

Authors: Gupta, Vedika | Singh, Vivek Kumar | Mukhija, Pankaj | Ghose, Udayan

Article Type: Research Article

Abstract: E-commerce websites provide an easy platform for users to put forth their viewpoints on different topics-ranging from a news item to any product in the market. Such online content encourages authors to express opinions on various aspects of an entity. Aspect based sentiment analysis deals with analyzing this textual content to look for the aspect in question. After locating the aspects, corresponding sentiment bearing words are looked for. This paper describes an integrated system that generates the opinionated aspect based graphical and extractive summaries from a large set of mobile reviews. The system focuses on three tasks (a) identification of …aspects in given field, (b) computation of sentiment polarity of each aspect, and (c) generates opinionated aspect based graphical and extractive summaries. The system has been evaluated on three mobile-reviews dataset and obtains better precision and recall than baseline approach. The system generates summaries from reviews without any training. Show more

Keywords: Aspect-based sentiment analysis, extractive summary, sentiment summarization

DOI: 10.3233/JIFS-179021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4721-4730, 2019

Authors: Baowaly, Mrinal Kanti | Tu, Yi-Pei | Chen, Kuan-Ta

Article Type: Research Article

Abstract: Online user reviews play an important role in the assessment of product quality, and thus these reviews should be evaluated carefully. This study evaluates the helpfulness of game reviews on the online Steam store. It collects a large set of user reviews of different game genres and builds a classification model to predict whether these reviews are helpful or not. This model can accurately predict the helpfulness of the reviews based on different thresholds. This work also investigates various types of textual and word embedding features and analyzed their importance for predictions. Furthermore, it develops a regression-based model that can …predict the score or rating of game reviews on Steam. Show more

Keywords: Steam, online review, review helpfulness, semantic analysis, word embedding

DOI: 10.3233/JIFS-179022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4731-4742, 2019

Authors: Frenda, Simona | Ghanem, Bilal | Montes-y-Gómez, Manuel | Rosso, Paolo

Article Type: Research Article

Abstract:  Patriarchal behavior, such as other social habits, has been transferred online, appearing as misogynistic and sexist comments, posts or tweets. This online hate speech against women has serious consequences in real life, and recently, various legal cases have arisen against social platforms that scarcely block the spread of hate messages towards individuals. In this difficult context, this paper presents an approach that is able to detect the two sides of patriarchal behavior, misogyny and sexism, analyzing three collections of English tweets, and obtaining promising results.

Keywords: Misogyny detection, sexism detection, linguistic analysis

DOI: 10.3233/JIFS-179023

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4743-4752, 2019

Authors: Alemán, Yuridiana | Somodevilla, María J. | Vilariño, Darnes

Article Type: Research Article

Abstract: In this paper an analysis, based on similarity metrics, was carried out in order to detect main concepts related to the superclasses in a pedagogical domain ontology. A semi-automatic corpus containing articles in Spanish was built. Afterward, the corpus was lemmatized and three representations were extracted. Four textual similarity metrics based on terms and Pointwise Mutual Information were implemented. A list of words, which was evaluated using a gold standard built by an expert in the domain, was retrieved from each experiment according to establish thresholds for the metrics. Precision and recall were used for evaluation step, where a detailed …discussion by representation and class was presented. Results showed a higher precision in types of intelligences class and 5-grams representation. Show more

Keywords: Ontology learning, pedagogical domain, NLP.

DOI: 10.3233/JIFS-179024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4753-4764, 2019

Authors: Buitrón, Edwar Javier Girón | Corrales, David Camilo | Avelino, Jacques | Iglesias, Jose Antonio | Corrales, Juan Carlos

Article Type: Research Article

Abstract:  The coffee rust is a devastating disease that causes large economic losses across the world. The severity of this disease changes over time so the farmers are not fully aware of the economic importance of the rust disease in the coffee crops. From a computational science perspective, several investigations have been proposed to decrease the effects caused by the coffee rust appearance from Expert systems based on machine learning techniques. However, because samples about coffee rust incidence are few, the rules created from machine learning techniques do not contain enough information to consider the diversity of scenarios for detecting coffee …rust. This paper proposes an expert system based on rules, where the rules are created considering the expert knowledge of specialists and technical reports about the behavior of the disease during a crop year. As far as we know, this is the first expert system proposed using not only expert knowledge but also technical reports in the coffee rust problem. The Buchanan methodology is used to design the proposed system. Experiment results present an average accuracy of 66,67% to detect a correct warning of coffee rust levels. Show more

Keywords: Decision support system, crops, disease, agriculture, hemileia vastatrix

DOI: 10.3233/JIFS-179025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4765-4775, 2019

Authors: Lithgow-Serrano, Oscar | Collado-Vides, Julio

Article Type: Research Article

Abstract: The constant increase in the production of scientific literature is making it very difficult for experts to keep up to date with the state-of-the-art knowledge in their fields. The use of Natural Language Processing (NLP) is becoming a necessary aid to tackle this challenge. In the NLP field, the task of measuring semantic similarity between two sentences plays a vital role. It is a cornerstone for tasks like Q&A, Information Retrieval, Automatic Summarization, etc., and it is a crucial element in the ultimate goal of computers being able to decode what is conveyed in human language expression. Measuring Semantic …Similarity (SS) in short texts has specific challenges. Because there are fewer words to be compared, the meaning contribution of each word is more relevant, and it is important to take into account the syntax’s contribution to the composed meaning. In addition, the highly specific and specialized vocabulary — Microbial Transcriptional-Regulation—implies the lack of massive training resources. Our approach has been to use an ensemble of similarity metrics including string, distributional, and knowledge-based metric and to combine the results of such analyses. We have trained and tested these methods in a similarity corpus developed in-house. The task has proved very challenging, and the ensemble strategy has proved to be a good approach. Even though there is still much room for improvement in the precision of our methods concerning the human evaluation, we have managed to improve them reaching a strong correlation (ρ  = 0.700). Show more

Keywords: Natural Language Processing, Semantic Textual Similarity

DOI: 10.3233/JIFS-179026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4777-4786, 2019

Authors: Dash, Sandeep Kumar | Saha, Saurav | Pakray, Partha | Gelbukh, Alexander

Article Type: Research Article

Abstract:  Caption generation requires best of both Computer Vision and Natural Language Processing. Due to recent improvements in both of them many efficient models have been developed. Automatic Image Captioning can be utilized to provide descriptions of website content or to engender frame-by-frame descriptions of video for the vision-impaired and in many such applications. In this work, a model is described which is utilized to generate novel image captions for a previously unseen image by utilizing a multimodal architecture by amalgamation of a Recurrent Neural Network (RNN) and a Convolutional Neural Network (CNN). The model is trained on Microsoft Common Objects …in Context (MSCOCO), an image captioning dataset that aligns captions and images in the same representation space, so that an image is close to its relevant captions in that space and far away from dissimilar captions and dissimilar images. ResNet-50 architecture is used for extracting features from the images and GloVe embeddings are used along with Gated Recurrent Unit (GRU) in Recurrent Neural Network (RNN) for text representation. MSCOCO evaluation server is used for evaluation of the machine generated caption for a given image. Show more

Keywords: Image captioning, convolutional neural network

DOI: 10.3233/JIFS-179027

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4787-4796, 2019

Authors: Majumder, Goutam | Pakray, Partha | Pinto, David

Article Type: Research Article

Abstract:  This work focuses on bolstering the pre–existing Interpretable Semantic Textual Similarity (iSTS) method, that will enable a user to understand the behaviour of an artificial intelligent system. The proposed iSTS method explains the similarities and differences between a pair of sentences. The objective of the iSTS problem is to formalize the alignment between a pair of text segments and to label the relationship between the text fragments with a relation type and relatedness score. The overall objective of this work is to develop a 1:M multi chunk aligner for an iSTS method, which is trained on SemEval 2016 Task …2 dataset. The obtained result outperforms many state–of–art aligners, which were part of SemEval 2016 iSTS task. Show more

Keywords: WordNet, interpretability, semantic semilarity, Natural Language Processing, cosine similarity

DOI: 10.3233/JIFS-179028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4797-4808, 2019

Authors: Srivastava, Jyoti | Sanyal, Sudip | Srivastava, Ashish Kumar

Article Type: Research Article

Abstract: Word reordering is an important problem for translation between languages which have different structures such as Subject-Verb-Object and Subject-Object-Verb. This paper presents a statistical method for extraction of linguistic rules using chunk to reorder the output of the baseline statistical machine translation system for improved performance. The experiments are based on the TDIL sample tourism corpus of English-Hindi language pair which consists of 1000 sentence pairs out of which 900 sentence pairs are used for training, 50 sentences for tuning and 50 sentences for testing. Finally, the output of the machine translation system, augmented by these rules, is evaluated by …using BLEU and NIST metrics. The BLEU score improves by more than 2% in comparison to the baseline SMT system. The results are compared with those of Google translation system which has been trained on a huge corpus. We got a 0.1 point improvement in terms of NIST score, in comparison to Google Translation. Thus, we have comparable results with such a small corpus of 900 sentence pairs for training. This paper is an effort to improve the performance of SMT with a small corpus by using linguistic rules where the rules are automatically generated instead of made by linguist. Show more

Keywords: Statistical machine translation, chunk, rule extraction, reordering rules, hybrid machine translation

DOI: 10.3233/JIFS-179029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4809-4819, 2019

Authors: Sengupta, Saptarshi | Pandit, Rajat | Mitra, Parag | Naskar, Sudip Kumar | Sardar, Mohini Mohan

Article Type: Research Article

Abstract: One of the most challenging research problems in natural language processing (NLP) is that of word sense induction (WSI). It involves discovering senses of a word given its contexts of usage without the use of a sense inventory which differentiates it from traditional word sense disambiguation (WSD). This paper reports a work on sense induction in Bengali, a less-resourced language, based on distributional semantics and translation based context vectors learned from parallel corpora to improve the task performance. The performance of the proposed method of sense induction was compared with the k-means algorithm, which was considered as the baseline in …our work. A dataset for sense induction was created for 15 Bengali words, encompassing a total of 111 contexts. The proposed model, in both mono and cross-lingual settings, outperformed k-means in precision (P), recall (R) and F-scores. K-means based sense induction produced average P, R and F-scores of 0.71, 0.73 and 0.66 respectively. The average P, R and F-scores produced by the mono-and cross-lingual settings of the proposed algorithm are 0.77, 0.73, 0.68 and 0.81, 0.77 and 0.72 respectively. Show more

Keywords: Word sense induction (WSI), parallel corpora, translation, Word2Vec, context clustering

DOI: 10.3233/JIFS-179030

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4821-4832, 2019

Authors: Ameer, Iqra | Sidorov, Grigori | Nawab, Rao Muhammad Adeel

Article Type: Research Article

Abstract: The process of automatic identification of an author’s demographic traits like gender, age, native language, geographical location, personality type and others from his/her written text is termed as author profiling (AP). Currently, it has engaged the research community due to its promising uses in security, marketing, forensic, bogus account identification on public networks. A variety of benchmark corpora (English text) released by PAN shared task is used to perform our experiments. This study presents a Content-based approach for detection of author’s traits (age group and gender) for same-genre author profiles. In our proposed method, we used a different set of …features including syntactic n-grams of part-of-speech tags, traditional n-grams of part-of-speech tags, the combination of word n-grams and combination of character n-grams. We tried a range of classifier for several profile sizes. We used the word uni-grams and character tri-grams as our baseline approaches. We achieved best accuracy of 0.496 and 0.734 for both traits, i.e., age group and gender respectively, by applying the combination of word n-grams of various sizes. Experimental results signify that the combination of word n-grams can produce good results on benchmark corpora. Show more

Keywords: Author profiling, machine learning, syntactic n-grams, traditional n-grams, part-of-epeech

DOI: 10.3233/JIFS-179031

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4833-4843, 2019

Authors: Gómez-Adorno, Helena | Fuentes-Alba, Roddy | Markov, Ilia | Sidorov, Grigori | Gelbukh, Alexander

Article Type: Research Article

Abstract: We present a method for gender and language variety identification using a convolutional neural network (CNN). We compare the performance of this method with a traditional machine learning algorithm – support vector machines (SVM) trained on character n-grams (n  = 3–8) and lexical features (unigrams and bigrams of words), and their combinations. We use a single multi-labeled corpus composed of news articles in different varieties of Spanish developed specifically for these tasks. We present a convolutional neural network trained on word- and sentence-level embeddings architecture that can be successfully applied to gender and language variety identification on a relatively small corpus …(less than 10,000 documents). Our experiments show that the deep learning approach outperforms a traditional machine learning approach on both tasks, when named entities are present in the corpus. However, when evaluating the performance of these approaches reducing all named entities to a single symbol “NE” to avoid topic-dependent features, the drop in accuracy is higher for the deep learning approach. Show more

Keywords: Convolutional neural networks, deep learning, author profiling, gender identification, language variety identification, machine learning, character n-grams, Spanish

DOI: 10.3233/JIFS-179032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4845-4855, 2019