Journal of ICT Research and Applications

Sentiment Classification for Film Reviews in Gujarati Text Using Machine Learning and Sentiment Lexicons

Parita Shah, Priya Swaminarayan, Maitri Patel — Tue, 11 Apr 2023 00:00:00 +0700

In this paper, two techniques for sentiment classification are proposed: Gujarati Lexicon Sentiment Analysis (GLSA) and Gujarati Machine Learning Sentiment Analysis (GMLSA) for sentiment classification of Gujarati text film reviews. Five different datasets were produced to validate the machine learning-based and lexicon-based methods’ accuracy. The lexicon-based approach employs a sentiment lexicon known as GujSentiWordNet, which identifies sentiments with a sentiment score for feature generation, while in the machine learning-based approach, five classifiers are used: logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), support vector machine (SVM), naive Bayes (NB) with TF-IDF, and count vectorizer for feature selection. Experiments were carried out and the results obtained were compared using accuracy, precision, recall, and F-score as performance evaluation criteria. According to the test results, the machine learning-based technique improved accuracy by 3 to 10% on average when compared to the lexicon-based approach.

The Potential of a Low-Cost Thermal Camera for Early Detection of Temperature Changes in Virus-Infected Chili Plants

Tue, 11 Apr 2023 00:00:00 +0700

One effect of viral infection on plant physiology is increased stomata closure so that the transpiration rate is low, which in turn causes an increase in leaf temperature. Changes in plant leaf temperature can be measured by thermography using high-resolution thermal cameras. The results can be used as an indicator of virus infection, even before the appearance of visible symptoms. However, the higher the sensor resolution of the thermal camera, the more expensive it is, which is an obstacle in developing the method more widely. This article describes the potential of thermography in detecting Tobacco mosaic virus infection in chili-pepper plants using a low-cost camera. A FLIR C2 camera was used to record images of plants in two treatment groups, non-inoculated (V0) and virus-inoculated plants (V1). Significantly, V1 had a lower temperature at 8 and 12 days after inoculation (dai) than those of V0, but their temperature was higher than V0 before symptoms were visible, i.e., at 17 dai. Thermography using low-cost thermal cameras has potency to detect early viral infection at 8 dai with accuracy levels (AUC) of 80.0% and 86.5% based on k-Nearest Neighbors and Naïve Bayes classifiers, respectively.

Emergency Data Transmission Mechanism in VANETs using Improved Restricted Greedy Forwarding (IRGF) Scheme

Kathirvelu Lakshmi, Manivasagam Soranamageswari — Mon, 17 Apr 2023 00:00:00 +0700

One of the most critical tasks in Vehicular Ad-hoc Networks (VANETs) is broadcasting Emergency Messages (EMs) at considerable data delivery rates (DDRs). The enhanced spider-web-like Transmission Mechanism for Emergency Data (TMED) is based on request spiders and authenticated spiders to create the shortest route path between the source vehicle and target vehicles. However, the adjacent allocation is based on the DDR only and it is not clear whether each adjacent vehicle is honest or not. Hence, in this article, the Improved Restricted Greedy Forwarding (IRGF) scheme is proposed for adjacent allocation with the help of trust computation in TMED. The trust and reputation score value of each adjacent vehicle is estimated based on successfully broadcast emergency data. The vehicles’ position, velocity, direction, density, and the reputation score, are fed to a fuzzy logic (FL) scheme, which selects the most trusted adjacent node as the forwarding node for broadcasting the EM to the destination vehicles. Finally, the simulation results illustrate the TMED-IRGF model’s efficiency compared to state-of-the-art models in terms of different network metrics.

Anomaly Detection for Human Home Activities Using Pattern Based Sequence Classification

Rawan Mohammed Elhadad, Yi-Fei Tan — Mon, 17 Apr 2023 00:00:00 +0700

In most countries, the old-age people population continues to rise. Because young adults are busy with their work engagements, they have to let the elderly stay at home alone. This is quite dangerous, as accidents at home may happen anytime without anyone knowing. Although sending elderly relatives to an elderly care center or hiring a caregiver are good solutions, they may not be feasible since it may be too expensive over a long-term period. The behavior patterns of elderly people during daily activities can give hints about their health condition. If an abnormal behavior pattern can be detected in advance, then precautions can be taken at an early stage. Previous studies have suggested machine learning techniques for such anomaly detection but most of the techniques are complicated. In this paper, a simple model for detecting anomaly patterns in human activity sequences using Random forest (RF) and K-nearest neighbor (KNN) classifiers is presented. The model was implemented on a public dataset and it showed that the RF classifier performed better, with an accuracy of 85%, compared to the KNN classifier, which achieved 73%.

Sociable Robot ‘Lometh’: Exploring Interactive Regions of a Product-Promoting Robot in a Supermarket

Nethmini T. Weerawarna, Udaka A. Manawadu, P. Ravindra S. De Silva — Fri, 28 Apr 2023 00:00:00 +0700

The robot ‘Lometh’ is an information-presenting robot that naturally interacts with people in a supermarket environment. In recent years, considerable effort has been devoted to the implementation of robotic interfaces to identify effective behaviors of communication robots focusing only on the social and physical factors of the addresser and the hearer. As attention focus and attention target shifting of people differs based on the human visual focus and the spatiality, this study considered four interactive regions, considering the visual focus of attention as well as the interpersonal space between robot and human. The collected primary data revealed that 56% attention shifts occurred in near peripheral field of view regions and 44% attention shifts in far peripheral field of view regions. Using correspondence analysis, we identified that the bodily behaviors of the robot showed the highest success rate in the left near peripheral field of view region. The verbal behaviors of the robot captured human attention best in the right near peripheral field of view region. In this experiment of finding a socially acceptable way to accomplish the attention attracting goals of a communication robot, we observed that the robots’ affective behaviors were successful in shifting human attention towards itself in both left and right far- peripheral field of view regions, so we concluded that for far field of view regions, designing similar interaction interventions can be expected to be successful.

The Utility of Decision Tree and Analytics Hierarchy Process in Prioritizing of Social Aid Distribution due to Covid-19 Pandemic in Indonesia

Sun, 30 Apr 2023 00:00:00 +0700

The Indonesian government provided various social assistance programs to local governments during Covid-19. One of the difficulties for the local governments in determining candidates for social aid is ensuring that the number of candidates is in balance with the available quota. Therefore, the local governments must select the most eligible candidates. This study proposes a priority model that can provide recommendations for candidates who meet the criteria for social assistance. The six parameters used in this study were: number of dependents, occupation, income, age, Covid status, and citizen status. The model operates in two stages, namely classification followed by ranking. The classification stage is conducted using a decision tree, while the ranking stage is performed conducted using the Analytical Hierarchy Process (AHP) algorithm. The decision tree separates two classes, namely, eligible and non-eligible. In addition, the classification process is also used to determine the dominant attributes and played a role in the modeling. The proposed model generates a list of the most eligible candidates based on our research. These are sorted by weight from greatest to most eligible using five dominant parameters: number of dependents, income, age, Covid status, and citizen status.

CNN Based Covid-19 Detection from Image Processing

Wed, 31 May 2023 00:00:00 +0700

Covid-19 is a respirational condition that looks much like pneumonia. It is highly contagious and has many variants with different symptoms. Covid-19 poses the challenge of discovering new testing and detection methods in biomedical science. X-ray images and CT scans provide high-quality and information-rich images. These images can be processed with a convolutional neural network (CNN) to detect diseases such as Covid-19 in the pulmonary system with high accuracy. Deep learning applied to X-ray images can help to develop methods to identify Covid-19 infection. Based on the research problem, this study defined the outcome as reducing the energy costs and expenses of detecting Covid-19 in X-ray images. Analysis of the results was done by comparing a CNN model with a DenseNet model, where the first achieved more accurate performance than the second.