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Artificial intelligence-based weather prediction framework using neural networks

10.11591/ijeecs.v36.i3.pp1836-1848
Keshav Kaushik , Gunjan Chhabra , Salil Bharany , Ateeq Ur Rehman , Habib Hamam
For humans, weather prediction is vital in making rational everyday choices and avoiding risk. Accurate weather forecasting is regarded as one of the world’s most difficult issues. New weather forecasting, unlike conventional techniques, relies on a mixture of computer simulations, observation (via balloons and satellites), and information of patterns and trends (via local weather analysts and weather stations). Predictions are rendered with fair precision using such techniques. Prediction algorithms based on complicated formulas run the majority of computational models used for prediction. This paper highlights the prediction of weather with the artificial neural networks (ANN) using the latest available smart computing devices. To assess the effectiveness of the model, comparison research is conducted with the other existing models in the same area. The result demonstrates that our approach is better in comparison to other similar research and products. The comparative analysis has been undergone which confirms the superiority of our proposed techniques with an accuracy of 90.4%.
Volume: 36
Issue: 3
Page: 1836-1848
Publish at: 2024-12-01

The relationship between principal inclusive leadership, teacher efficacy and teachers’ innovative behavior

10.11591/ijere.v13i6.29187
Mo Dongxiao , Suhaida Abdul Kadir , Roshafiza Hassan , Enio Kang Mohd Sufian Kang
The objective of this study was to explore the relationship among principals’ inclusive leadership, teachers’ sense of efficacy, and teachers’ innovative behaviors. A sample of 671 middle school teachers from Guangxi, China was utilized. The study revealed that the practice of inclusive leadership by principals had a positive effect on teacher efficacy. Furthermore, teacher efficacy appeared to have a beneficial effect on teacher innovative behavior. Additionally, the study showed that principal inclusive leadership significantly influenced teacher innovative behavior. Lastly, it proved that teacher efficacy played a mediating role in the relationship between principal inclusive leadership and teacher innovative behavior. The study offers strategies for enhancing teacher’s propensity for innovative conduct. When designing programs to promote teacher innovative behavior, it is important to take into account the impact of inclusive leadership by principals and the magnitude of teacher efficacy.
Volume: 13
Issue: 6
Page: 4338-4345
Publish at: 2024-12-01

A low-cost localization method in autonomous vehicle by applying light detection and ranging technology

10.11591/ijeecs.v36.i3.pp1739-1749
Raju Jagadeesh Kannan , Malothu Amru , Surulivelu Muthumarilakshmi , Jeyaprakash Jeyapriya , Stalin Aghalya , Dhakshnamoorthy Muthukumaran , Subbiah Murugan
The autonomous platform uses global positioning system (GPS) to localize the vehicle. In addition, light detection and ranging (LIDAR) and the high precision camera help to identify the turns in the road. The proposed system can help to determine the road turns with higher accuracy without utilizing LIDAR and high-precision camera technology. This research aims to implement a cost-effective simultaneous localization system that can reduce the cost by half for any autonomous vehicle. The existing system is more complex due to the inclusion of LIDAR technology. In contrast, the proposed approach uses beacon communication between vehicles and infrastructure and long-range (LoRa) for vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) communication. The simulation result illustrates that the proposed approach provides better performance.
Volume: 36
Issue: 3
Page: 1739-1749
Publish at: 2024-12-01

Impact of high-k insulators on electrical properties of junctionless double gate strained transistor

10.11591/ijeecs.v36.i3.pp1437-1447
Khairil Ezwan Kaharudin , Fauziyah Salehuddin , Anis Suhaila Mohd Zain , Nabilah Ahmad Jalaludin , Faiz Arith , Siti Aisah Mat Junos , Ibrahim Ahmad
High-k dielectric insulators are required to reduce leakage and increase transistor performance. They are able to impact the mobility of carriers in transistors positively, leading to better device performance in advanced transistor architecture. Nevertheless, an in-depth analysis of how high-k dielectric insulators influence transistor characteristics must be carried out to determine their suitability. The objective of this study is to investigate the impact of high-k insulators towards electrical properties of junctionless double gate strained transistor. The simulation works is done using process/device simulator Silvaco Athena/Atlas. Based on the retrieved results, the magnitude of ION, on-off ratio, gm, and Cint for TiO2-based device are approximately 63%, 99%, 62%, and 89% respectively higher than the lowest permittivity material-based device. The TiO2-based device also exhibits the lowest magnitude in IOFF and SS compared to others. However, a significant degradation in fT magnitude have been observed for TiO2-based device significantly due to its large capacitances
Volume: 36
Issue: 3
Page: 1437-1447
Publish at: 2024-12-01

Enhancing road image clarity with residual neural network dehazing model

10.11591/ijai.v13.i4.pp4147-4157
Aerun Martin , Mohd Nazeri Kamaruddin , Zamani Md Sani , Hadhrami Abdul Ghani
Lane markers, or road markers, are the painted lines on a roadway that separate different lanes of traffic. Lane markers guide drivers and ensure orderly vehicle flow. They are essential for advanced driver assistance systems (ADAS), providing reference points for vehicle positioning on the road. These markers enable ADAS to give warnings, assistance, and automation features that enhance driver safety and convenience. However, unpredictable illumination, such as a foggy environment, can suppress marker visibility, impacting ADAS's performance. Deep learning-based methods are well-known for their superiority in handling various haze patterns. This paper presents a residual network (ResNet)-based deep learning model to improve road image clarity impacted by fog. The residual neural network dehaze model (RNN-D) utilises a joint loss function to produce haze-free images with improved lighting conditions and enhanced details. The model was trained, validated, and fine-tuned using hazy and corresponding non-hazy datasets to ensure that the model is quantitatively superior in the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). RNN-D achieved an average PSNR of 27.98 and SSIM of 0.8 on multiple open sourced datasets. The proposed algorithm's superior performance and visually appealing results make it a powerful tool for real-world image dehazing applications.
Volume: 13
Issue: 4
Page: 4147-4157
Publish at: 2024-12-01

User perceptions of artificial intelligence powered phishing attacks on Facebook's resilient infrastructure

10.11591/ijaas.v13.i4.pp878-886
JosephNg Poh Soon , Rou Qian Chan , Qian Hui Lee , Dick En Loke , Stevenson Ling Heng Chun , Phan Koo Yuen
This study focuses on examining the user perceptions of a cybersecurity certificate transparency (CT) monitoring tool in the context of artificial intelligence (AI) powered phishing attacks on the Facebook platform. Implementing CT monitoring tools is one strategy for preventing these attacks. It reveals a significant level of concern among respondents regarding the potential risks associated with phishing attacks, indicating a growing awareness of the severity of such threats for future resilient infrastructure development. Users' knowledge and understanding of AI-driven phishing threats were found to vary, emphasizing the need for awareness campaigns towards sustainable development education. The study also highlights varying levels of confidence among users in effectively identifying and thwarting phishing efforts, suggesting the importance of user empowerment through improved training, tools, and technologies as responsive institutions. These findings underscore the significance of addressing user concerns, enhancing security awareness, and providing users with the necessary resources to protect themselves against sophisticated phishing attacks. The research contributes to the understanding of user perceptions and lays the groundwork for further improvements in security measures and user education in the fight against phishing threats on Facebook's inclusive growth.
Volume: 13
Issue: 4
Page: 878-886
Publish at: 2024-12-01

Real-time smoke and fire detection using you only look once v8-based advanced computer vision and deep learning

10.11591/ijaas.v13.i4.pp987-999
Shakila Rahman , Syed muhammad Hasnat Jamee , Jakaria Khan Rafi , Jafrin Sultana Juthi , Abdul Aziz Sajib , Jia Uddin
Fire and smoke pose severe threats, causing damage to property and the environment and endangering lives. Traditional fire detection methods struggle with accuracy and speed, hindering real-time detection. Thus, this study introduces an improved fire and smoke detection approach utilizing the you only look once (YOLO)v8-based deep learning model. This work aims to enhance accuracy and speed, which are crucial for early fire detection. The methodology involves preprocessing a large dataset containing 5,700 images depicting fire and smoke scenarios. YOLOv8 has been trained and validated, outperforming some baseline models- YOLOv7, YOLOv5, ResNet-32, and MobileNet-v2 in the precision, recall, and mean average precision (mAP) metrics. The proposed method achieves 68.3% precision, 54.6% recall, 60.7% F1 score, and 57.3% mAP. Integrating YOLOv8 in fire and smoke detection systems can significantly improve response times, enhance the ability to mitigate fire outbreaks, and potentially save lives and property. This research advances fire detection systems and establishes a precedent for applying deep learning techniques to critical safety applications, pushing the boundaries of innovation in public safety.
Volume: 13
Issue: 4
Page: 987-999
Publish at: 2024-12-01

Signalling overhead minimization aware handover execution using ensemble learning in next generation wireless networks

10.11591/ijai.v13.i4.pp4281-4290
Bhavana Srinivas , Nadig Vijayendra Uma Reddy
Upcoming smart intelligent heterogeneous wireless networks (HWNs) and their uses can greatly benefit from the merging of long-term evolution (LTE) sub-6 GHz along with millimeter wave (mmWave) frequencies by boosting the coverage, bandwidth, reliability, seamless connectivity, and high quality of service (QoS). Nevertheless, because of the inability of directed waves in terms of coverage, it is difficult to locate the appropriate mmWave remote radio units (RRUs). Therefore, it is crucial to lessen the burden of the handover signaling processes. In meeting research requirements this paper presents signaling overhead minimization aware handover execution (SOMAHE) model. The SOMAHE model first introduces a novel handover mechanism between LTE and mmWave is presented in this research, followed by a machine learning (ML)-based autonomous handover execution technique. To estimate the handover success rate, the model introduces a feature ensemble learning (FEL) model built using XGBoost (XGB) model that makes use of sampling windows channel data. To conclude, combining FEL into the SOMAHE model reduces signaling overhead while simultaneously increasing the handover success-rate. Experiment results with varying mobile terminals, demonstrate that the SOMAHE model significantly outperforms the existing standard deep q-networks (DQN)-based handover-execution method.
Volume: 13
Issue: 4
Page: 4281-4290
Publish at: 2024-12-01

Early detection of coronary heart disease based on risk factors using interpretable machine learning

10.11591/ijaas.v13.i4.pp944-956
Wiharto Wiharto , Farah Nada Mufidah
Coronary heart disease (CHD) is the leading cause of death in the world. The risk of coronary heart disease can be reduced or even prevented by early detection. Early detection of CHD has been widely developed using machine learning, but the machine learning algorithms used sometimes have low interpretability. Low interpretability makes it difficult for users to understand the cause of the decision. Referring to this, this research aims to propose an early detection model using machine learning interpretability, which is implemented using the C5.0 algorithm and interpreted using Shapley additive explanations (SHAP). This research method is divided into 3 stages, namely preprocessing, interpretable machine learning, and performance evaluation. This study used 215 patient data from Dr. Moewardi Surakarta Hospital. Testing the resulting model using the k-folds cross-validation method. The test results show that the risk factors that make a high contribution to the output of the coronary heart disease detection model are systolic blood pressure, diastolic blood pressure, and employment level, with the resulting accuracy performance of 84.64%. The proposed model can be an alternative for early prediction of coronary heart disease which can explain the influence of each selected risk factor on the model output.
Volume: 13
Issue: 4
Page: 944-956
Publish at: 2024-12-01

High-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos

10.11591/ijeecs.v36.i3.pp1827-1835
Mukesh Kumar Tripathi , Chellapilla V. K. N. S. N. Moorthy , Sandeep Kadam , Chaitali Shewale , Priya Shelke , Pravin R. Futane
Unmanned aerial vehicles (UAVs) and sophisticated deep learning (DL) models have made the application of artificial intelligence (AI) more popular. This has resulted in an increase in the number of attempts to improve high-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos. The study introduces a one-class support vector machine (OC-SVM) oddity locator for low-altitude, limited-scope UAVs used for ethereal video surveillance. The primary goal is to improve UAV-based observation capabilities by identifying areas or things of interest without prior knowledge, hence improving tasks like queue control, vehicle following, and hazardous product identification. The framework makes use of OC-SVM because of its quick and lightweight setup, making it suitable for continuous operation on low-computational UAVs. It empowers the identification of several peculiarities necessary for low-elevation reconnaissance by using textural characteristics to recognise both large-scale and tiny structures. Examine the UAV mosaicking and change location (UMCD) dataset to demonstrate the effectiveness of the framework, which achieves excellent accuracy and outperforms traditional methods by about one fifth in a variety of metrics. The suggested model compares with current methods, demonstrating superior accuracy and performance in recognition of peculiarities. Evaluation metrics include F1-score, review, exactness, and accuracy. The model demonstrates that it always encounters an oddity with a review compromise of up to seven on ten, achieving complete accuracy.
Volume: 36
Issue: 3
Page: 1827-1835
Publish at: 2024-12-01

Enhancing interpretability in random forest: Leveraging inTrees for association rule extraction insights

10.11591/ijai.v13.i4.pp4054-4061
Fatma Hilali Moh’d , Khairil Anwar Notodiputro , Yenni Angraini
The random forest model is a powerful supervised learner, recognized for its ability to learn the pattern within data with superior predictive accuracy. However, it is a black box model because it lacks interpretability. This study addressed the interpretable challenge by employing the inTree framework. The rules were extracted from each decision tree in a random forest model, and the association rules were determined through measured matrix support and confidence to reveal the frequent variable interactions for predicting unemployment. This approach provided insight into the relationships between specific variables and unemployment outcomes. The developed method used data set from the integrated labor force survey (ILFS) 2020/2021 in Zanzibar. Zanzibar’s unemployment rate consistently increased across surveys conducted in 2006, 2014, and 2020/2021. Results have shown that the rules that most predict unemployment for individuals are female and lack of health insurance and secondary education level, female and youth age group and lack of health insurance and secondary education level with a high confidence level. This study provides practical insights for Zanzibar’s government to develop effective interventions, programs, and policies. Improving the interpretability of the random forest model enhances decision-making to address unemployment challenges.
Volume: 13
Issue: 4
Page: 4054-4061
Publish at: 2024-12-01

Data-driven decisions: Artificial intelligence-based experimental validation of ocean ecosystem services scale

10.11591/ijai.v13.i4.pp4178-4185
Ronnie Figueiredo , Pedro Cabral
Several studies address the main topic of research, ecosystem services. It is also proven that decision-making in organizations generally involves a decision-maker, who assumes internal responsibility for the results. However, when the decision is collective, we need to think about the context of governance. How can we increase the sustainable decisions of ocean ecosystem services governance? When this decision is applied to ocean ecosystem services, in particular, we need a parameter. Therefore, the proposed scale is an initial guide for support key decision-makers decisions on the governance of ocean services ecosystems. The scale proposal with validation through classical linear regression, and supported by an artificial neural network, demonstrates the main variables that influence the decision and contribute to possible risk mitigations in terms of decisions. 
Volume: 13
Issue: 4
Page: 4178-4185
Publish at: 2024-12-01

Impact study on indirect lightning strikes on photovoltaic systems near transmission lines

10.12928/telkomnika.v22i6.26613
Anis Niza; Universiti Teknikal Malaysia Melaka Ramani , Rayyan Che; Suria & Sonne Sdn. Bhd Razali , Aine Izzati; Universiti Teknikal Malaysia Melaka Tarmizi
Grid-integrated photovoltaic (PV) systems are currently undergoing explosive growth in Malaysia. However, as more PV systems are installed close to transmission lines, there are concerns about the impact of electromagnetic (EM) properties affecting the performance and operation of the PV systems if they are exposed to lightning strikes, particularly indirect ones. Therefore, this study aims to model the impact of indirect lightning strikes on PV systems installed in proximity to transmission lines. The model involves developing a 3D model of the PV system together with a sample transmission line and creating an artificial lightning event to study the EM activity within the area. The results are compared to the IEC 61000 standard to determine its level of r\hazard. From the simulation results, it was found that the current intensity, H, of the disturbance, can reach up to 63% more than the standard limit stipulated in the standards. The significance of the study ensures that PV systems installed within the vicinity of power lines or substations have adequate lightning protection systems as well as proper earthing systems.
Volume: 22
Issue: 6
Page: 1581-1589
Publish at: 2024-12-01

Digital learning using ChatGPT in elementary school mathematics learning: a systematic literature review

10.11591/ijeecs.v36.i3.pp1701-1710
Prabandari Listyaningrum , Heri Retnawati , Harun Harun , Hamidulloh Ibda
Digital learning with ChatGPT in elementary school mathematics learning is urgently implemented. Several studies have explored digital learning with ChatGPT in elementary school mathematics learning, but studies using SLR are minimal. This article presents the 2022-2024 SLR study on digital learning with ChatGPT in elementary school mathematics learning. This SLR and PRISMA method is supported by Publish or Perish 8, VOSviewer version 1.6.20, Mendeley version 1.19.8, and ATLAS.ti version 7.5.16. The search results obtained 1,259 Scopus articles, which were filtered to 40 and analyzed using ATLAS.ti, then the results were described according to the research question. Digital learning with ChatGPT is a learning approach using the synchronous-asynchronous mode, virtual classrooms, distance, use of interactive digital tools, digital methods and media, innovation, digital modeling, use of robotics and AI ChatGPT for children with the principle of collaboration digital, and problem-solving with the support of digital resources. ChatGPT features multilingual, natural language, advanced AI, 24/7 availability, answering math questions, recurring training, and helping students with various math tasks. Implementation of digital learning with ChatGPT in elementary school mathematics learning for problem-solving, geometry, function limits in algebra, the material on flat shapes, geometric shapes, integrated PjBL, online, mixed and flipped classes.
Volume: 36
Issue: 3
Page: 1701-1710
Publish at: 2024-12-01

Evaluating metaverse platforms for educational purposes: a heuristic evaluation study

10.11591/ijaas.v13.i4.pp1019-1027
Cadelina Cassandra , Mohamad Noorman Masrek , Fadhilah Aman
This research's primary objective is to investigate the educational potential by investigating the metaverse website, emphasizing from users' perspectives. It is critical to examine the functionality to identify any potential faults in the learning experience because of the complexities of metaverse apps, which include 3D virtual environments and complex interactions. This study uses heuristic evaluation, which is a useful method for analyzing application success by utilizing well-established design principles and best practices in user interface and user experience design. To conduct this study, a panel of five experts, each with outstanding competence in the topic, was assembled to test the metaverse website in an educational setting. The evaluation was built on the ten heuristic evaluation standards, which served as a solid framework for measuring its usability and functionality. According to the findings of this examination, the average severity number of heuristic issues is one. This result indicates that the website is under "cosmetic problems only." While this implies that specific areas need to be addressed to improve overall quality, it also means that these difficulties do not take precedence over other vital ones. As a result, the website has significant promise as a metaverse instructional platform, and changes can be made to further improve its efficiency.
Volume: 13
Issue: 4
Page: 1019-1027
Publish at: 2024-12-01
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