Forthcoming and Online First Articles

International Journal of Intelligent Systems Technologies and Applications

International Journal of Intelligent Systems Technologies and Applications (IJISTA)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are published online here, before they appear in a journal issue. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

We also offer which provide timely updates of tables of contents, newly published articles and calls for papers.

International Journal of Intelligent Systems Technologies and Applications (4 papers in press)

Regular Issues

  • Integration of Deep Learning Techniques for Sentiment and Emotion Analysis of Social Media Data   Order a copy of this article
    by H.S. Hota, Dinesh Sharma, Nilesh Verma 
    Abstract: : Sentiment Analysis (SA) and Emotion Analysis (EA) are commonly used to understand people's feelings and opinions on a given topic. COVID-19 is an emerging infectious disease that is rapidly spreading around the world. The mental state of a country's population is more or less the same worldwide. Machine Learning (ML) techniques are commonly utilized to analyze human sentiments and emotions. Two popular Deep Learning (DL) techniques: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), are being applied in several areas. In this study, we propose a hybrid of CNN and LSTM to improve the performance of the classification model. The two different models, the Sentiment Analysis Model (SAM) and the Emotional Analysis Model (EAM), were developed using benchmark data, which produces 91.11% and 89.39% accuracy, respectively, by integrating CNN and LSTM. Integration of two or more techniques significantly improves performance by utilizing both techniques. The results of the experiments demonstrate that the proposed hybrid technique outperforms other individual DL techniques.
    Keywords: Convolutional Neural Network (CNN); Sentiment Analysis (SA); Emotion Analysis (EA); COVID-19; Deep Learning (DL); Long Short-Term Memory (LSTM).

  • Access Selection in Heterogeneous Wireless Networks Based on User Preferences   Order a copy of this article
    by Jamal Haydar, Abed Ellatif Samhat, Guy Pujolle 
    Abstract: Access selection is an important key in heterogeneous networks, and the design of a new algorithm for decision is not a trivial task. Different aspects must be taken into consideration while designing a new decision algorithm, including both users requirements (in terms of resources, QoS, users preferences), and operator policies that aim to maximize the utilization of its network capacity and to deliver services with acceptable QoS levels for the largest number of customers. Thus, in this paper we propose a new selection algorithm based on users preferences. The comparison between the proposed scenarios is given based on several performance indicators. The results show the improvement achieved by increasing the resource utilization and therefore the overall system capacity.
    Keywords: access selection; heterogeneous networks; user preferences; QoS; resource utilization.

  • DAILY ACTIVITY MONITORING SYSTEM DESIGNED FOR ELDERLY PEOPLE USING HIDDEN MARKOV MODELS BASED ON REAL WORLD DATASETS   Order a copy of this article
    by Chaima Bouali, Olivier Habert, Abderrahim Tahiri 
    Abstract: The worlds elderly population has considerably increased in previous years and is on course to reach about 2 billion in 2050 (Ageing and health, 2022). Ambient Assisted Living (AAL) aims to cope with the growing need for seniors health care services. It has been determined that anomaly detection, in Activity of Daily Living (ADL), is a crucial feature of senior assistive technologies (Li et al., 2015; Ruano et al., 2019). This work describes how our in situ abnormal behaviour detection system functions for elderly people (aged 60 and older) in their home. Our research is based on the data gathered by a domotic box that is available for purchase. The box was initially intended to continuously detect the owners daily actions using non-intrusive home automation sensors. The enhancement of the detection of the health changes, deducted by the abnormal behaviour in the daily activities of the user, is closely related to the evolution of the activity recognition of the box. Our system aims to report a relevant context-aware alert to health care service experts. By refining the detection of the activity level of the occupants, we could identify warning manifestations for early intervention. In this paper, we will describe the process of pointing out irregularity in the daily activity pattern of a user or the detection of a malfunction of the box to maintain the continuity of the service it offers with pertinent data.
    Keywords: Activity monitoring; ambient assisted living; data processing; smart homes; elderly; assisted living; sensors; statistical approach.

  • Gesture based mouse control system based on MPU6050 and Kalman filter technique   Order a copy of this article
    by Narayanan Prasanth, Kaustubh Shrivastava, Abhishek Sharma, Aritri Basu, Ritwik A Sinha, RAJA S P 
    Abstract: A computer mouse is a handheld pointing device used to control the cursor in most Graphical User based computer systems. Due to disorders like carpal tunnel syndrome faced by extensive mouse users, there is a scope for the development of gesture based pointing devices. In the current market, only a few stand-alone devices are available for wearable gesture recognition, yet most of them are expensive and not appropriate for comfortable daily wear. Most of them do not have an affordable cost for large scale adoption. Modern gesture recognition algorithms are mostly designed for discrete gestures. The recognition of gestures with accuracy is still a widespread challenge which leads to prevention in the usage of wearable gesture recognition systems. In this paper, we proposed a Gesture Based Mouse, a smart glove based system for gesture recognition and remote control. To reduce the dependency of the gesture recognition system on external conditions like light and network connection, we eliminate the need of a camera and use a glove fitted with the MPU6050 sensor to detect motion accurately with minimal latency. The elimination of the camera component also reduced the overall component cost significantly. In this paper, we discuss the algorithm followed to map hand motion to cursor movement on the screen via the pseudocode and flowchart. Using this methodology, basic functionalities of the mouse and its operations were implemented with less expense and to more effectiveness. Finally, this paper discusses various challenges faced in the development of the algorithm and offers suggestions for future development in this domain.
    Keywords: Kalman filter; Gesture based mouse; NodeMCU; MPU6050; PyAutoGUI.