Journal of Intelligent & Fuzzy Systems - Volume 39, issue 6

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8039-8039, 2020

Authors: Varadarajan, Vijayakumar | Kommers, Piet | Piuri, Vincenzo | Subramaniyaswamy, V.

Article Type: Editorial

DOI: 10.3233/JIFS-189309

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8041-8042, 2020

Authors: Chen, Lihui | Zhang, Rongzhu | Ahmad, Awais | Jeon, Gwanggil | Yang, Xiaomin

Article Type: Research Article

Abstract: Data cognition plays an important role in cognitive computing. Cognition of low-resolution (LR) image is a long-stand problem because LR images have insufficient information about objects. For better cognition of LR images, a multi-resolution residual network (MRRN) is proposed to improve image resolution in this paper for cognitive computing systems. In MRRN, a multi-resolution feature learning (MRFL) strategy is introduced to achieve satisfying performance with low computational costs. Inspired by image pyramids, a feature pyramid is designed to implement multi-resolution feature learning in the building unit of the proposed MRRN. Specifically, multi-resolution residual units (MRRUs) are introduced as the building …units of the proposed network, which consist of a feature pyramid decomposition stage and a feature reconstruction stage. To obtain informative features, transferred skip links (TSLs) are utilized to transfer fine-grain residual features in the pyramid decomposition stage to the reconstruction stage. The effectiveness of MRFL and TSL is demonstrated by ablation experiments. Also, the tests on standard benchmarks indicate the superiority of the proposed MRRN over other state-of-the-art methods. Show more

Keywords: Artificial intelligence, deep learning, convolutional neural networks, computer vision, image super-resolution

DOI: 10.3233/JIFS-189127

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8043-8055, 2020

Authors: Sirajudeen, Mohamed | Anitha, R.

Article Type: Research Article

Abstract: Manually verifying the authenticity of the physical documents (personal identity card, certificates, passports, legal documents) increases the administrative overhead and takes a lot of time. Later image processing techniques were used. But most of the image processing based forgery document detection methods are less accurate. To improve the accuracy, this paper proposes an automatic document verification model using Convolutional Neural Networks (CNN). Furthermore, we use Optical Character Recognition (OCR) and Linear Binary Pattern (LBP) to extract the textual information and regional edges from the documents. Later, Oriented fast and Rotated Brief (ORB) is used to extract the images from the …scanned documents. To train the CNN, MIDV-500 dataset of 256 Azerbaijani passport images, each with the size of 1040*744 pixels is taken. The proposed CNN model uses sliding window operations layers to evaluate the authenticity. The proposed model analyzes both the textual authenticity and image (seal, stamp and hologram) authenticity of the scanned document. The experimental analysis is carried out on the TensorFlow using python programming language. The results derived from the proposed CNN based forgery detection model is compared with existing models and the results are promising to implement on the real time applications Show more

Keywords: Document verification, convolution neural network, forgery document detection, cognitive document processing

DOI: 10.3233/JIFS-189128

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8057-8068, 2020

Authors: Dhanalakshmi, R. | Sri Devi, T.

Article Type: Research Article

Abstract: Cognitive computing is the mirroring of human brain and this is made possible by using natural language processing, pattern recognition and data mining. By mirroring the human brain (Cognitive computing system), helps to solve some of the complicated problems without much of human supervision. In the fast-changing world, the major challenge every organization facing is difficulty in retaining its employees. Employees may leave an organization due to low salary, overwork, lack of opportunities and recognition, work culture, work-life imbalance etc. Better ways to retain employees is to understand their requirements and fulfill them. The proposed employee feedback sentiment analysis system …collects the employee feedback reviews from open forums and perform sentiment analysis using Recurrent Neural Network – Long Short-term Memory (RNN-LSTM) algorithm. On performing Sentiment analysis, employee review comments are classified as Positive or Negative. A report is generated and sent to the HR of the organization as webapp or mobile app. The report has total number of positive and negative comments and positive and negative counts with respect to salary, work pressure etc. With the report, the organization can arrive at identifying social sentiments of their brand and may take corrective actions to retain employees which benefits both organization and employees. This paper also captures the performance of various models in training and predicting the employee feedback dataset and the models evaluated are Logistic Regression, Support Vector Machine, Random Forest Classifier, AdaBoost Classifier, Gradient Boosting Classifier, Decision Tree Classifier and Gaussian Naïve Bayes. The classification report and accuracy of each model is captured. The dataset size was gradually increased from 200 to 1000 and accuracy was predicted for each model. It was identified that the accuracy of machine learning algorithms was ranging between 66% to 85%. On training RNN-LSTM algorithm with dataset of size 30 k, the accuracy was 88%. It was identified that Deep learning algorithm RNN-LSTM performs better with huge dataset. Increasing dataset size still increase the performance of RNN-LSTM algorithm in training and prediction. Thus, the objective function of the proposed model to perform sentiment analysis on employee feedback review comments is achieved successfully. Show more

Keywords: Cognitive, deep learning, RNN-LSTM, LSTM, recurrent neural network

DOI: 10.3233/JIFS-189129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8069-8078, 2020

Authors: Shanthi, P. | Umamakeswari, A.

Article Type: Research Article

Abstract: Cloud computing is gaining ground in the digital and business world. It delivers storage service for user access using Internet as a medium. Besides the numerous benefits of cloud services, migrating to public cloud storage leads to security and privacy concerns. Encryption method protects data privacy and confidentiality. However, encrypted data stored in cloud storage reduces the flexibility in processing data. Therefore, the development of new technologies to search top representatives from encrypted public storage is the current requirement. This paper presents a similarity-based keyword search for multi-author encrypted documents. The proposed Authorship Attribute-Based Ranked Keyword Search (AARKS) encrypts documents …using user attributes, and returns ranked results to authorized users. The scheme assigns weight to index vectors by finding the dominant keywords of the specific authority document collection. Search using the proposed indexing prunes away branches and processes only fewer nodes. Re-weighting documents using the relevant feedback also improves user experience. The proposed scheme ensures the privacy and confidentiality of data supporting the cognitive search for encrypted cloud data. Experiments are performed using the Enron dataset and simulated using a set of queries. The precision obtained for the proposed ranked retrieval is 0.7262. Furthermore, information leakage to a cloud server is prevented, thereby proving its suitability for public storage. Show more

Keywords: Cloud storage, data privacy, ranked retrieval, index tree, authorized retrieval

DOI: 10.3233/JIFS-189130

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8079-8089, 2020

Authors: Dayal, Rasbihari | Vijayakumar, V. | Kushwaha, Rahul Chandra | Kumar, Abhishek | Ambeth Kumar, V. D. | Kumar, Ankit

Article Type: Research Article

Abstract: This research paper presents a cognitive model which manages to minimize the issues of the Information Technology Infrastructure by incorporation of service management practices. The importance of this research is that this model can be replicated in other companies for the distribution of products that wish to implement improvements in their management process technological services. This work introduces the use of Information Technology Infrastructure Library or ITIL as best practice, essential methodologies for IT Management, historical evolution, methodology, service life cycle, and ITIL certifications. Service automation is widely regarded as the usefulness and improves service guarantee. One of the most …useful features of automation services is that the process will run the same way every time. Such precision in the execution of repetitive executions is virtually impossible when it comes to human labor. Therefore, the automation is the best way to improve the efficiency of the service provider and the next steps of the process. Show more

Keywords: ITIL V3, IT service management, adoption, ITSM, ITC

DOI: 10.3233/JIFS-189131

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8091-8102, 2020

Authors: Latif, Ghazanfar | Alghazo, Jaafar | Maheswar, R. | Vijayakumar, V. | Butt, Mohsin

Article Type: Research Article

Abstract: The agriculture industry is of great importance in many countries and plays a considerable role in the national budget. Also, there is an increased interest in plantation and its effect on the environment. With vast areas suitable for farming, countries are always encouraging farmers through various programs to increase national farming production. However, the vast areas and large farms make it difficult for farmers and workers to continually monitor these broad areas to protect the plants from diseases and various weather conditions. A new concept dubbed Precision Farming has recently surfaced in which the latest technologies play an integral role …in the farming process. In this paper, we propose a SMART Drone system equipped with high precision cameras, high computing power with proposed image processing methodologies, and connectivity for precision farming. The SMART system will automatically monitor vast farming areas with precision, identify infected plants, decide on the chemical and exact amount to spray. Besides, the system is connected to the cloud server for sending the images so that the cloud system can generate reports, including prediction on crop yield. The system is equipped with a user-friendly Human Computer Interface (HCI) for communication with the farm base. This multidrone system can process vast areas of farmland daily. The Image processing technique proposed in this paper is a modified ResNet architecture. The system is compared with deep CNN architecture and other machine learning based systems. The ResNet architecture achieves the highest average accuracy of 99.78% on a dataset consisting of 70,295 leaf images for 26 different diseases of 14 plants. The results obtained were compared with the CNN results applied in this paper and other similar techniques in previous literature. The comparisons indicate that the proposed ResNet architecture performs better compared to other similar techniques. Show more

Keywords: Automatic plant identification, residual networks, cognitive vision drone, deep learning, automatic spraying, Convolutional Neural Networks (CNN), smart devices, plant diseases.

DOI: 10.3233/JIFS-189132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8103-8114, 2020

Authors: Randhawa, Princy | Shanthagiri, Vijay | Kumar, Ajay

Article Type: Research Article

Abstract: In the new era of technology with the development of wearable sensors, it is possible to collect data and analyze the same for recognition of different human activities. Activity recognition is used to monitor humans’ activity in various applications like assistance for an elderly and disabled person, Health care, physical activity monitoring, and also to identify a physical attack on a person etc. This paper presents the techniques of classifying the data from normal activity and violent attack on a victim. To solve this problem, the paper emphasis on classifying different activities using machine learning (supervised) techniques. Various experiments have …been conducted using wearable inertial fabric sensors for different activities. These wearable e-textile sensors were woven onto the jacket worn by a healthy subject. The main steps which outline the process of activity recognition: location of sensors, pre-processing of the statistical data and activity. Three supervised algorithmic techniques were used namely Decision tree, k-NN classifier and Support Vector Machine (SVM). Based on the experimental work, the results obtained show that the SVM algorithm offers an overall good performance matched in terms of accuracy i.e. 97.6% and computation time of 0.85 seconds for k-NN and Decision Tree for all activities. Show more

Keywords: Fabric sensors, accelerometer, woman protection, algorithm, machine learning

DOI: 10.3233/JIFS-189133

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8115-8123, 2020

Authors: Christy, Jackson J | Rekha, D | Vijayakumar, V | Carvalho, Glaucio H.S.

Article Type: Research Article

Abstract: Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal …solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis. Show more

Keywords: VANET, evolutionary algorithm, genetic algorithm, cuckoo search, firefly algorithm, MAC protocol

DOI: 10.3233/JIFS-189134

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8125-8137, 2020