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31,458 Article Results

Forecasting research influence: a recurrent neural network approach to citation prediction

10.11591/ijeecs.v36.i2.pp1070-1082
Naser Jamal , Mohammad Alauthman , Muhannad Malhis , Abdelraouf M. Ishtaiwi
As the volume of scientific publications continues to proliferate, effective evaluation tools to determine the impact and quality of research articles are increasingly necessary. Citations serve as a widely utilized metric for gauging scientific impact. However, accurately prognosticating the long-term citation impact of nascent published research presents a formidable challenge due to the intricacy and unpredictability innate to the scientific ecosystem. Sophisticated machine learning methodologies, particularly recurrent neural networks (RNNs), have recently demonstrated promising potential in addressing this task. This research proposes an RNN architecture leveraging encoder-decoder sequence modeling capabilities to ingest historical chronicles and predict succeeding evolution via latent temporal dynamics learning. Comparative analysis between the RNN approach and baselines, including random forest, support vector regression, and multi-layer perceptron, demonstrate superior performance on unseen test data and rigorous k-fold cross-validation. On a corpus from Petra University, the RNN methodology attained the lowest errors (root mean squared error (RMSE) 1.84) and highest accuracy (0.91), area under the curve (AUC) (0.96), and F1-score (0.92). Statistical tests further verify significant improvements. The findings validate our deep learning solution's efficacy, robustness, and real-world viability for long-term scientific impact quantification to aid stakeholders in research evaluation. The findings intimate that RNN-based predictive modeling constitutes a potent technology for citation-driven scientific impact quantification.
Volume: 36
Issue: 2
Page: 1070-1082
Publish at: 2024-11-01

Timing issues on power side-channel leakage of advanced encryption standard circuits designed by high-level synthesis

10.11591/ijres.v13.i3.pp616-624
Yuto Miura , Hiroki Nishikawa , Xiangbo Kong , Hiroyuki Tomiyama
In recent years, field programmable gate array (FPGA) have been used in many internet of things (IoT) devices and are equipped with cryptographic circuits to ensure security. However, they are exposed to the risk of cryptographic keys being stolen by side-channel attacks. Countermeasures against side-channel attacks have been developed, but they are becoming more of a threat to IoT devices due to the diversity of attacks. Therefore, it is necessary to understand the basic characteristics of side-channel attacks. Therefore, this study clarifies the relationship between two timing issues, the clock period of the circuit and the power sampling interval, and the amount of side-channel leakage. We design seven advanced encryption standard (AES) circuits with different clock periods and conduct empirical experiments using logic simulations to clarify the correlation between the two timings and the amount of side-channel leakage. T-test is used to evaluate the leakage amount, which is evaluated based on four metrics. From the results, we argue that the clock period and sampling interval do not interfere with each other in the side-channel leakage amount.
Volume: 13
Issue: 3
Page: 616-624
Publish at: 2024-11-01

Enhancing phishing URL detection through comprehensive feature selection: a comparative analysis across diverse datasets

10.11591/ijeecs.v36.i2.pp1182-1188
Preeti Preeti , Priti Sharma
Malicious attacks have developed a prominent risk to the safety of online users, with attackers employing increasingly sophisticated systems to deceive unsuspecting victims. This research focuses on the critical aspect of feature selection in optimizing phishing uniform resource locator (URL) detection system. Feature selection boosts machine learning (ML) and deep learning (DL) by picking vital attributes efficiently. This research paper provides a comprehensive examination of feature selection techniques using five diverse datasets. Various methods, including random forest (RF) select from model, SelectKBest with chi-square statistic, principal component analysis (PCA) and recursive feature elimination (RFE), were employed. The experiments, with a particular emphasis on PCA and fourth dataset, revealed that all four models RF, decision trees (DTs), XGBoost, and multilayer perceptron) achieved 100% accuracy in detecting phishing URL attacks. This underscores the efficacy of feature selection methods in enhancing to a deeper understanding of feature selection’s role in bolstering the effectiveness of phishing detection system across diverse datasets, highlighting the importance of leveraging techniques such as PCA for optimal results.
Volume: 36
Issue: 2
Page: 1182-1188
Publish at: 2024-11-01

Improving the performance of IoT devices that use Wi-Fi

10.11591/ijres.v13.i3.pp748-757
Ali Ahmed Razzaq , Kunjam Nageswara Rao
Providing quality service to users of the internet of things (IoT) entails addressing two crucial aspects: one related to security and the other concerning the limited resources of IoT devices. We will face a challenge while using timesensitive applications within a network that utilizes a high-performance Wi-Fi technology with exceeding energy consumption. Due to this research challenge, we propose a new algorithm, IoT-quality of service (QoS), designed to achieve a true balance between enhancing the security aspects of IoT devices and improving network-hardware performance. Thus, the algorithm efficiently manages the limited energy resources by monitoring energy levels, communication quality, and queuing delay at access points. This is accomplished by utilizing a streamlined identity management system capable of achieving authentication and access authorization with reduced loading for IoT devices. The research hypothesis underwent validation through a comparative analysis of its performance against the conventional model of a Wi-Fi-based IoT device. This evaluation was conducted utilizing the NS3 simulator and was based on a predetermined set of parameters influencing the examined performance metrics, including power consumption, throughput, delay, and response time. The findings exposed the superiority of the proposed algorithm.
Volume: 13
Issue: 3
Page: 748-757
Publish at: 2024-11-01

Optimizing network lifetime in wireless sensor networks: a hierarchical fuzzy logic approach with LEACH integration

10.11591/ijeecs.v36.i2.pp1140-1148
Chandrika Dadhirao , Ram Prasad Reddy Sadi , B V A N S S Prabhakar Rao , Panduranga Vital Terlapu
Wireless sensor networks (WSNs) are of significant importance in many applications; nevertheless, their operational efficiency and longevity might be impeded by energy limitations. The low energy adaptive clustering hierarchy (LEACH) protocol has been specifically developed with the objective of achieving energy consumption equilibrium and regularly rotating cluster heads (CHs). This study presents a novel technique, namely the hierarchical fuzzy logic controller (HFLC), which is integrated with the LEACH protocol to enhance the process of CH selection and effectively prolong the network's operational lifespan. The HFLC system employs fuzzy logic as a means to address the challenges posed by uncertainty and imprecision. It assesses many aspects, including residual energy, node proximity, and network density, in order to make informed decisions. The combination of HFLC with LEACH demonstrates superior performance compared to the conventional LEACH protocol in terms of energy efficiency, stability, and network durability. This study emphasizes the potential of intelligent and adaptive mechanisms in improving the performance of WSNs by improving the survivability of nodes by reducing the energy consumption of the nodes during the communication of network process. It also paves the way for future research that integrates soft computing approaches into network protocols.
Volume: 36
Issue: 2
Page: 1140-1148
Publish at: 2024-11-01

An interactive visualization tool for the exploration and analysis of multivariate ocean data

10.11591/ijeecs.v36.i2.pp1329-1337
Preetha K. G. , Saritha S. , Jishnu Jeevan , Chinnu Sachidanandan , P. A. Maheswaran
Ocean data exhibits great heterogeneity from variances in measuring methods, formats, and quality, making it extremely complicated and diverse due to a variety of data kinds, sources, and study elements. A few examples of data sources are satellites, buoys, ships, self-driving cars, and distant systems. The processing of data is made more challenging by the significant regional and temporal variations in oceanic characteristics including temperature, salinity, and currents. This work presents an interactive tool for multivariate ocean parameter visualisation, specifically overlays, based on Python. In ocean data visualisation, overlays are extra visual layers or data points that are layered to improve comprehension over a basic map. Based on the available data and the visualisation goals, these overlays are chosen and blended. Users can customise overlays with this tool, which also supports formatting, 2D and 3D visualisation, and data preparation. In order to reduce artefacts, it uses kriging interpolation for 3D visualisation and a modified version of the ray casting algorithm for representing octree data. By integrating overlays like as bathymetry, currents, temperature, and marine life, users can produce visually appealing and comprehensive depictions of ocean data. This method provides a thorough grasp of intricate marine processes by making it easier to see patterns, trends, and abnormalities in the data.
Volume: 36
Issue: 2
Page: 1329-1337
Publish at: 2024-11-01

Characterization of UF-18 cacao pods using Arduino-based load compressor testing machine

10.11591/ijeecs.v36.i2.pp741-748
Maricel Gamolo Dayaday , Renel M. Alucilja , Ritchell Joy T. Cuarteros , Jeffrey A. Lavarias
Bean damage is one of the primary concerns in the pod-breaking process. Studies for pod-breaking machines are ongoing to ensure that the products made from these machines are of good quality. The objective of the study is to determine the physical and mechanical characteristics of the UF-18 pod. The Arduino-based load compressor testing machine was designed and developed to characterize the UF-18 pod. It was found that the average geometric mean diameter, surface area, and sphericity index of 115.37 mm, 41,899.48 mm², and 0.6372, respectively, and with a variation of ±27.17, ±14538133.04, and ±0.00038 respectively. Furthermore, the cacao pod samples had an average dimension of 181.29 mm, 94.26 mm, 90.01 mm, and 17.44 mm measured for the length, equatorial diameter, intermediate diameter and external thickness, respectively. Different pod sizes and thicknesses require various forces ranging from 36.94 to 92.42 kg (362.38 N to 906.64 N) and time ranging from 6-11 seconds to be able to break the pods. Determining the physical and mechanical properties of cacao pods enables fabricators to design efficient machines, which lessens the force to break and the damage to the beans, thus producing quality beans.
Volume: 36
Issue: 2
Page: 741-748
Publish at: 2024-11-01

Auto digitization of aerial images to map generation from UAV feed

10.11591/ijeecs.v36.i2.pp1338-1346
Raju Jagadeesh Kannan , Karunesh Pratap Yadav , Balasubramanian Sreedevi , Jehan Chelliah , Surulivelu Muthumarilakshmi , Jeyaprakash Jeyapriya , Subbiah Murugan
Nowadays the rapid growth of unmanned aerial vehicles (UAVs) bridges the space between worldly and airborne photogrammetry as well as allow flexible acquisition of great solution images. In the case of natural disasters such as floods, tsunamis, earthquakes, and cyclones, their effects are most often felt in the micro-spaces and urban environments. Therefore, rescuers have to go around to get to the victims. This paper presents an auto digitization of aerial images to map generation from UAV feed at night time. In case of a power outage and an absence of alternative light sources, rescue operations are also slowed due to the darkness caused by the lack of electricity and the inability to light additional sources. In other words, to save lives, we need to know about all essential large-scale feature spaces in the dark so that we can use this information in times of disaster. The research proposed a soft framework for crisis mapping to aid in mapping the state of the aerial landscape in disaster-stricken areas, allowing strategic rescue operations to be more effectively planned.
Volume: 36
Issue: 2
Page: 1338-1346
Publish at: 2024-11-01

Development of an IoT-based sleep pattern monitoring system for sleep disorder detection

10.11591/ijeecs.v36.i2.pp777-784
Muhammad Nur Ikhwan Md Shahrum , Ida Syafiza Md Isa , Nur Latif Azyze Mohd Shaari Azyze , Haslinah Mohd Nasir , Tole Sutikno
Inadequate sleep can cause various health problems including heart disease and obesity. In this work, a sleep monitoring system that monitors human sleep patterns is developed using the internet of things (IoT) and Raspberry Pi. The system is designed to record any detected movements and process the data using machine learning to provide valuable insight into a person’s sleep patterns including sleep duration, the time taken to fall asleep, and the frequency of waking up. This information is very useful to provide the sleep disorder diagnostics of an individual including restless leg, parasomnia and insomnia syndrome besides giving recommendations to improve their sleep quality. Also, the system allows the processed data to be stored in the cloud database which can be accessed through a mobile application or web interface. The performance of the system is evaluated in terms of its accuracy and reliability in detecting sleep order diagnostics. Based on the confusion matrix, the results show the accuracy of the system is 90.32%, 91.80%, and 91.80% in detecting the restless leg, parasomnia and insomnia syndrome, respectively. Meanwhile, the system showed high reliability in monitoring 10 participants for 8 hours and updated the recorded data and its analysis in the cloud.
Volume: 36
Issue: 2
Page: 777-784
Publish at: 2024-11-01

Channel reconstruction through improvised deep learning architecture for high-speed networks

10.11591/ijres.v13.i3.pp786-798
Parinitha Jayashanka , Byrappa Nanjundaiah Shobha
Efficient acquisition of channel state information (CSI) is quite complicated process but immensely essential to exploit probable benefits of massive multiple input multiple output (MIMO) systems. Therefore, a deep learningbased model is utilized to estimate channel feedback in a massive MIMO system. The proposed improvised deep learning-based channel estimation (IDLCE) model enhances channel reconstruction efficiency by using multiple convolutional layers and residual blocks. The proposed IDLCE model utilizes encoder network to compress CSI matrices where decoder network is used to downlink reconstruct CSI matrices. Here, an additional quantization block is incorporated to improve feedback reconstruction accuracy by reducing channel errors. A COST 2,100 model is adopted to analyse performance efficiency for both indoor and outdoor scenarios. Further, deep learning-based model is used to train thousands of parameter and correlation coefficients much faster and to minimize computational complexity. The proposed IDLCE model evaluate performance in terms of normalized mean square error (NMSE), correlation efficiency and reconstruction accuracy and compared against varied state-of-art-channel estimation techniques. Excellent performance results are obtained with large improvement in channel reconstruction accuracy
Volume: 13
Issue: 3
Page: 786-798
Publish at: 2024-11-01

Design of IoT-based monitoring system for temperature and dissolved oxygen levels in catfish aquaculture pond water

10.11591/ijres.v13.i3.pp687-698
Nurma Sari , Yuniar Savitri , Sri Cahyo Wahyono , Joko Santoso , Amar Vijai Nasrulloh
One of the fish in Indonesian waters that has been successfully bred and cultivated is the catfish (Pangasius sp.). In catfish farming, there are several water quality factors that need to be considered, such as temperature and dissolved oxygen levels. Based on the existing description, it is very important to pay attention to the water quality of aquaculture ponds, especially temperature and dissolved oxygen levels for fish survival. This study aims to create an internet of things (IoT) based monitoring system for temperature and dissolved oxygen levels in catfish aquaculture pond water based on NodeMCU ESP8266. Monitoring system is using SEN0237 gravity analog dissolved oxygen sensor, DS18B20 sensor module, NodeMCU ESP8266, 20×4-character liquid-crystal display (LCD), micro secure digital (SD) card module, internet modem. Data from measurements of temperature and dissolved oxygen levels are stored online in the Adafruit.io database in the .csv format and on a micro secure digital (SD) card in the device in the .txt format. The lowest value of dissolved oxygen levels and temperature are 3.4 mg/L or 3.4 ppm and a temperature of 27.9 °C, respectively. Meanwhile, the highest value of dissolved oxygen levels and temperature are 4.6 mg/L or 4.6 ppm and temperature of 30.9 °C, respectively.
Volume: 13
Issue: 3
Page: 687-698
Publish at: 2024-11-01

Effects of TiO2 in graphene-quantum-dot film on lighting color uniformity of a white light-emitting diodes

10.11591/ijeecs.v36.i2.pp800-807
Phan Xuan Le , Pham Hong Cong
Improvement in color uniformity of white light-emitting diodes (WLED) is one of the imperative goals for high-quality solid-state illumination. The conventional WLED model with a single yellow phosphor YAG:Ce3+ (TiO2@GD) is proposed to fulfill this goal. The TiO2@GD composites prove to possess excellent biocompatibility, low toxicity, and thermal and chemical stability, holding great potential in high-power WLED production. By maintaining a constant GDs content of 10 wt%, the research explores the impact of varying TiO2 doping concentrations on the lighting performance of the WLEDs via the mean of light scattering. The TiO2@GD layer also induces a red-shift in the emitted light spectrum, contributing to a reduction in color variation. While a decline in luminosity and color rendering performance becomes evident with excessive TiO2 content, the study underscores the potential of TiO2@GD as a viable diffusing layer for LEDs to obtain improved angular uniformity of color distribution.
Volume: 36
Issue: 2
Page: 800-807
Publish at: 2024-11-01

Air quality monitoring system based on low power wide area network technology at public transport stops

10.11591/ijres.v13.i3.pp699-707
Ricardo Yauri , Bill Loayza , Alvaro Yauri , Anyela Aquino
Mass migration from rural areas to urban areas has caused problems of traffic congestion, high industrial concentration and inequity in the distribution of housing in the world's capitals, generating a significant threat to sustainable development and public health due to air pollution air. In the Peruvian context, the importance of real-time monitoring of air quality is highlighted according to the standards established by the government. Several studies propose real-time environmental monitoring systems using internet of thing (IoT) technologies, electrochemical and optical sensors to measure pollutants, highlighting the need for data analysis. The objective of the paper is to show the implementation of IoT devices called sensor nodes, with long range wide area network (LoRaWAN) transmission technology for continuous monitoring of polluting gas concentrations. In addition, they are integrated into a central node called gateway to perform real-time monitoring through a web application. As an initial result, IoT devices demonstrated their effectiveness for real-time monitoring. Despite being a prototype-level result, the next stage involves its deployment at public transport stops in Lima. Overcoming the limitations of the solution, this paper establishes the foundation for future research on pollution and public health.
Volume: 13
Issue: 3
Page: 699-707
Publish at: 2024-11-01

Electroencephalography-based wheelchair navigation control using convolutional neural network method

10.12928/telkomnika.v23i1.25988
Khairul; University of Jember Anam , Satrio; University of Jember Marta Wicaksono , Muchamad; University of Jember Arif Hana Sasono , Bima; University of Jember Wahyu Maulana , Fatkhul; University of Jember Mubarok , Ananta; University of Jember Pinsentius Rahmat Pamungkas , Moch.; University of Jember Rijal Fatoni
Artificial intelligence refers to a computer-based system capable of learning human activities. For instance, in medical technology, AI can be used for a thought-controlled wheelchair. This study discusses the use of deep learning, specifically convolutional neural network (CNN), in predictiong of the user intention to navigate a wheelchair. The training data was collected from an EEG sensor and included the wheelchair’s movements - turning right, turning left, moving forward, moving backward, and idle. The signals were then sampled and feature-extracted using root mean square (RMS). In CNN classification, both raw and RMS data were used. This study compared two different CNN architectures. The first architecture has three convolutional layers and three pooling layers, while the second has two of each. The research compares the accuracy and loss values of CNN predictions using architecture 1 and 2 on both raw and RMS data. The experimental results indicate that when using raw data, the first CNN architecture achieved an accuracy of 85.12%, and the second model achieved 91.04%. However, when using RMS data, the first architecture achieved an accuracy of 76.47%, and the second achieved 73.74%. The study concludes that the movement of the wheelchair is better in real-time when using raw data compared to using RMS data.
Volume: 23
Issue: 1
Page: 147-155
Publish at: 2024-10-26

Designing a marketplace system to assist incubators in higher education in fostering technopreneurship

10.12928/telkomnika.v23i1.26452
Cepy; UIN Sunan Gunung Djati Slamet , Ichsan; UIN Sunan Gunung Djati Budiman , Muhammad Insan; Monash University Al-Amin , Muhammad Ali; UIN Sunan Gunung Djati Ramdhani , Dudang; UIN Sunan Gunung Djati Gojali
Technology-based startups play a vital role in boosting the Indonesian economy. However, many face sustainability challenges due to misalignment with market needs and a lack of comprehensive understanding of business processes. This condition demands the application of academic theories related to increasing productivity and highlights the need for innovative solutions. Universities can significantly support the development of these startups by leveraging business incubators to enhance competitiveness in the digital era. This research aims to propose a marketplace system architecture incorporating a business incubator unit (BIU), integrating scientific knowledge with societal business interests and talents in a university. The system is developed by the enterprise architecture (EA) methodology with the the open group architecture framework (TOGAF). This approach ensures a systematic design process that aligns with best practices. This study results in several strategic artefacts that provide guidance for creating a practical environment for entrepreneurs and translating theoretical insights into actionable business strategies. The artefacts can be rigorously tested and applied in real-world business incubator settings. The proposed system addresses critical challenges startup businesses face, such as enhancing business understanding and providing a fair foundation before entering the competitive market.
Volume: 23
Issue: 1
Page: 90-107
Publish at: 2024-10-18
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