Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

29,939 Article Results

Memory management of firewall filtering rules using modified tree rule approach

10.11591/ijict.v14i1.pp141-152
Dhwani Hakani , Palvinder Singh Mann
Firewalls are essential for safety and are used for protecting a great deal of private networks. A firewall’s goal is to examine every incoming and outgoing data before granting access. A notable kind of conventional firewall is the rule-based firewall. However, when it comes to job performance, traditional listed-rule firewalls are limited, and they become useless when utilized with some networks that have extremely big firewall rule sets. This study proposes a model firewall architecture called “TreeRule Firewall,” which has benefits and functions effectively in large-scale networks like “cloud.” In order to improve cloud network security, this study suggests a modified tree rule firewall (MTRF cloud) that eliminates rule discrepancies. For the matching firewall policy, this work creates a tree rule firewall. There are no duplicate rules created by the proposed improved tree rule firewall. Also, memory utilization of different size rules is compared.
Volume: 14
Issue: 1
Page: 141-152
Publish at: 2025-04-01

Learning high-level spectral-spatial features for hyperspectral image classification with insufficient labeled samples

10.11591/ijai.v14.i2.pp1211-1219
Douglas Omwenga Nyabuga , Godfrey Nyariki
Hyperspectral image (HSI) classification research is a hot area, with a mass of new methods being developed to improve performance for specific applications that use spatial and spectral image material. However, the main obstacle for scientists is determining how to identify HSIs effectively. These obstacles include an increased presence of redundant spectral information, high dimensionality in observed data, and limited spatial features in a classification model. To this end, we, therefore, proposed a novel approach for learning high-level spectral-spatial features for HSI classification with insufficient labeled samples. First, we implemented the principal component analysis (PCA) technique to reduce the high dimensionalities experienced. Second, a fusion of 2D and 3D convolutions and DenseNet, a transfer learning network for feature learning of both spatial-spectral pixels. The achieved experimental results are comparatively satisfactory to contrasted approaches on the widely used HSI images, i.e., the University of Pavia and Indian Pines, with an overall classification accuracy of 97.80% and 97.60%, respectively.
Volume: 14
Issue: 2
Page: 1211-1219
Publish at: 2025-04-01

Improving visual perception through technology: a comparative analysis of real-time visual aid systems

10.12928/telkomnika.v23i2.26455
Othmane; Sidi Mohamed Ben Abdellah University Sebban , Ahmed; De Vinci Higher Education, De Vinci Research Center Azough , Mohamed; Sidi Mohamed Ben Abdellah University Lamrini
Visually impaired individuals continue to face barriers in accessing reading and listening resources. To address these challenges, we present a comparative analysis of cutting-edge technological solutions designed to assist people with visual impairments by providing relevant feedback and effective support. Our study examines various models leveraging InceptionV3 and V4 architectures, long short-term memory (LSTM) and gated recurrent unit (GRU) decoders, and datasets such as Microsoft Common Objects in Context (MSCOCO) 2017. Additionally, we explore the integration of optical character recognition (OCR), translation tools, and image detection techniques, including scale-invariant feature transform (SIFT), speeded-up robust features (SURF), oriented FAST and rotated BRIEF (ORB), and binary robust invariant scalable keypoints (BRISK). Through this analysis, we highlight the advancements and potential of assistive technologies. To assess these solutions, we have implemented a rigorous benchmarking framework evaluating accuracy, usability, response time, robustness, and generalizability. Furthermore, we investigate mobile integration strategies for real-time practical applications. As part of this effort, we have developed a mobile application incorporating features such as automatic captioning, OCR based text recognition, translation, and text-to-audio conversion, enhancing the daily experiences of visually impaired users. Our research focuses on system efficiency, user accessibility, and potential improvements, paving the way for future innovations in assistive technology.
Volume: 23
Issue: 2
Page: 249-370
Publish at: 2025-04-01

Detecting fake news through deep learning: a current systematic review

10.12928/telkomnika.v23i2.26110
Idza Aisara; Universiti Malaysia Terengganu Norabid , Masita; Universiti Malaysia Terengganu Jalil , Rozniza; Universiti Malaysia Terengganu Ali , Noor Hafhizah; Universiti Malaysia Terengganu Abd Rahim
This systematic review explores the domain of deep learning-based fake new detection employing advanced search practices on Scopus and Web of Science (WoS) databases with keywords “fake news,” “deep learning,” and “method.” The study encompasses 33 articles categorized into three main themes: i) dataset and benchmarking for fake news detection, ii) multimodal approaches for fake news detection, and iii) deep learning applications and techniques for fake news detection. The analysis reveals the significance of curated datasets and robust benchmarking in improving the efficacy of fake news detection models. Additionally, the review highlights the emergence of multimodal approaches that integrate textual and visual information for improved detection accuracy. The findings clarify the essential role of deep learning applications, emphasizing the development of sophisticated models for automated identification of fake news. This systematic study adds to a thorough grasp of current research trends and offers insightful information for future developments in the field of deep learning-based false news identification.
Volume: 23
Issue: 2
Page: 329-339
Publish at: 2025-04-01

Enhancing spam detection using Harris Hawks optimization algorithm

10.12928/telkomnika.v23i2.26615
Mosleh; Al-Ahliyya Amman University M. Abualhaj , Sumaya; Al-Ahliyya Amman University Nabil Alkhatib , Ahmad; Al-Ahliyya Amman University Adel Abu-Shareha , Adeeb; Al-Ahliyya Amman University M. Alsaaidah , Mohammed; Universiti Sains Malaysia (USM) Anbar
This paper employs machine learning (ML) algorithms to identify and classify spam emails. The Harris Hawks optimization (HHO) algorithm can detect the crucial features that distinguish spam from ham emails. The HHO algorithm decreased the number of features in the ISCX-URL2016 spam dataset from 72 to 10. Implementing this will enhance the efficiency and cognitive acquisition of the ML algorithms. The decision tree (DT), Naive Bayes (NB), and AdaBoost algorithms are evaluated and contrasted to identify spam emails. The random search algorithm is used to optimize the significant hyperparameters of each algorithm for the specific task of spam identification. All three ML algorithms showed exceptional accuracy in detecting spam emails during the conducted testing. The DT algorithm attained a remarkable accuracy rate of 99.75%. The AdaBoost algorithm ranks second with an incredible accuracy of 99.67%. Finally, the NB algorithm attained an accuracy of 96.30%. The results demonstrate that the HHO algorithm shows promise in recognizing the crucial features of spam emails.
Volume: 23
Issue: 2
Page: 447-454
Publish at: 2025-04-01

Advanced crop yield prediction using machine learning and deep learning: a comprehensive review

10.12928/telkomnika.v23i2.26621
Ayush; Manipal University Jaipur Anand , Kavita; Manipal University Jaipur Jhajharia
The advancement of machine learning (ML) and deep learning (DL) techniques has significantly improved crop yield prediction, making it more accurate and reliable. In this review, the implementation of ML and DL algorithms for crop yield prediction is thoroughly investigated, focusing on their crucial role in enhancing crop productivity. Along with ML and DL algorithms examine, the review analyses the use of remote sensing technologies, such as satellite and drone data, in providing high-resolution inputs essential for accurate yield predictions. The study identifies the state of art algorithms, most used features, data sources and evaluation metrics, providing a comparison of ML and DL. The findings indicate that DL models are more effective with large datasets, while ML models remain robust for smaller datasets. The future directions are proposed to develop the generalised models for different crops and regions. The review aims to assist researchers by summarising state of art techniques and identifying the present.
Volume: 23
Issue: 2
Page: 402-415
Publish at: 2025-04-01

Homogeneous transformation matrix for force-torque sensor orientation compensation in rotatable control handle

10.12928/telkomnika.v23i2.26465
Shivam; COEP Technological University (COEP Tech) Suresh Zagade , Rajiv; COEP Technological University (COEP Tech) Basavarajappa H. , Sudhir; COEP Technological University (COEP Tech) Madhav Patil , Abhishek; Philips India Limited Pradeep Buzruk , Kshitij; Philips India Limited Ghanshyam Jiwane , Tole; Universitas Ahmad Dahlan Sutikno
The high inertia ceiling suspended systems with multiple degrees of freedom uses power assist technologies to reduce operator’s burden to operate the machine. Such systems are popularly used in medical diagnostic systems, construction machines, material handling, automotive, and aerospace assembly lines. These systems commonly use multi-axis force-torque (FT) sensor to sense the forces applied by user on rotatable control handle. These sensed forces are utilized by power assist algorithm to drive system in required direction with the help of electrical motor drives. The rotatable control handle used to control the machine poses a significant obstacle for maintaining alignment between FT sensor co-ordinate frame and the system’s base frame. This research paper focuses on the development of homogeneous transformation matrix to compensate for any change in FT sensor orientation caused by rotation of control handle. The homogeneous transformation matrix developed in this research paper, transforms the force and torque values measured by FT sensor with respect to system base frame. This adaptive technique provided seamless control of the power assist ceiling suspended system from different directions during handling and movement. This helped to enhance control and flexibility of power assist ceiling suspended system.
Volume: 23
Issue: 2
Page: 507-525
Publish at: 2025-04-01

Factors influencing the integration of web accessibility in Moroccan public e-services

10.11591/ijict.v14i1.pp77-90
Chadli Fatima Ezzahra , Aniss Moumen , Driss Gretete , Zineb Sabri
Governments worldwide are increasingly digitizing their services to enhance efficiency, transparency, and accessibility for citizens. Morocco has made significant strides in adopting information and communication technology (ICT) and has implemented various initiatives to promote digital transformation across sectors. However, ensuring that digital content and e-services are accessible to everyone, including people with disabilities, is crucial to building an inclusive digital environment. Against this background, this study, based on a qualitative analysis, explores the main factors influencing the integration of web accessibility in the Moroccan public sector from the perspective of web developers and information technology (IT) managers. Through semi-structured interviews and thematic analysis, the findings reveal key barriers such as limited awareness, training deficiencies, and lack of legal framework and available guidelines. Additionally, the study highlights the need for robust managerial backing and greater collaboration with stakeholders, including people with disabilities. By raising awareness and providing actionable insights, this study offers valuable recommendations for policymakers and moves the field forward, providing a foundation for future strategies to enhance web accessibility in the Moroccan public sector.
Volume: 14
Issue: 1
Page: 77-90
Publish at: 2025-04-01

Imposing neural networks and PSO optimization in the quest for optimal ankle-foot orthosis dynamic modelling

10.12928/telkomnika.v23i2.25876
Annisa; Universiti Malaysia Sarawak Jamali , Aida Suriana; Universiti Malaysia Sarawak Abdul Razak , Shahrol; Shibaura Institute of Technology Mohamaddan
Individuals with abnormal walking patterns due to various conditions face significant challenges in daily activities, especially walking. Ankle-foot orthosis (AFO) devices are crucial in providing essential support to their lower limbs. Accurately modeling the dynamic behavior of AFO systems, particularly in predicting ground reaction forces, is a complex yet vital task to ensure their effectiveness. This research develops dynamic models for AFO systems using advanced modeling techniques, employing both parametric and non-parametric approaches. Parametric methods, such as particle swarm optimization (PSO), and non-parametric methods, like multi-layer perceptron (MLP) neural networks, are utilized through system identification methods. According to the findings, the MLP neural network continuously generates objective results and performs exceptionally well in correctly detecting the AFO system, attaining a noticeably lower mean squared prediction error of 0.000011. This research highlights the potential of advanced modeling techniques, particularly MLP neural networks, in enhancing AFO system modeling accuracy. Although parametric techniques like PSO are useful, the MLP approach performs better, offering insightful information about modelling AFO systems and indicating that non-parametric techniques like MLP neural networks have potential to further AFO creation and control.
Volume: 23
Issue: 2
Page: 484-494
Publish at: 2025-04-01

The 360° beach video: a supporting mindfulness intervention with virtual reality

10.11591/ijict.v14i1.pp250-258
Rohmatus Naini , Mungin Eddy Wibowo , Edy Purwanto , Mulawarman Mulawarman , E. Oos M. Anwas
This article describes optimizing virtual reality (VR) with a 360° beach video model used for mindfulness interventions. Using VR with 360° beach videos to support the presence of an immersive environment can effectively support mindfulness practices. Students are interested in the integration of technology in school counseling. VR helps in creating immersive environments such as forests, beaches, waterfalls, etc. so that students focus more on practicing mindfulness and attention in the current moment. This article focuses on optimizing 360° beach videos in the breathing mindfulness process so that it helps bring out real experiences. Obstacles to practicing mindfulness include lack of focus, mind wandering and not concentrating. through the use of 360° beach videos with VR can increase focus and be more effective in mindfulness practice.
Volume: 14
Issue: 1
Page: 250-258
Publish at: 2025-04-01

Analyzing the impact of sports activity intensity on muscle capacity through integrated biosensor technology

10.12928/telkomnika.v23i2.26264
Ervin; Bandung State Polytechnic Masita Dewi , Nurista; Bandung State Polytechnic Wahyu Kirana , Sugondo; Telkom University Hadiyoso
In the past few years, biosensor technology has paved the way for new insights into the physiological effects of physical exercise. Quantitative analysis, especially in the case of muscle capacity measurement, is the focus of studies to assess the impact of sports activities. Therefore, this study examines the impact of sports activity intensity on muscle capacity using an integrated biosensor system developed at Bandung State Polytechnic. Surface electromyography (sEMG) measurements were conducted on 30 participants aged 20–25 during various sports activities. Results showed a strong positive correlation (r=0.814) between sports activity frequency and muscle contraction, suggesting higher activity correlates with increased muscle activity. Conversely, the correlation during muscle relaxation was low (r=0.261), indicating independence from sports activity. In the future, it is expected that integrated biosensors will have the ability to concurrently measure and monitor various parameters like heart rate (via electrocardiogram), blood oxygen levels (via photoplethysmography), and blood pressure. The integrated biosensor system allows for comprehensive assessment and optimization of sports performance and injury prevention strategies.
Volume: 23
Issue: 2
Page: 466-472
Publish at: 2025-04-01

Dual-band MIMO antenna for wideband THz communication in future 6G applications

10.12928/telkomnika.v23i2.26553
Jamal; Daffodil International University Hossain Nirob , Kamal; Daffodil International University Hossain Nahin , Md. Ashraful; Daffodil International University Haque , Md. Sharif; Daffodil International University Ahammed , Narinderjit Singh; INTI International University Sawaran Singh , Redwan; Daffodil International University A. Ananta , Md. Kawsar; Daffodil International University Ahmed , Liton; Pabna University of Science and Technology Chandra Paul
This paper presents an industrial and innovation dual-band multiple-input multiple-output (MIMO) antenna designed for terahertz (THz) frequencies to enhance future sixth-generation (6G) communication systems. The antenna utilizes a polyimide substrate with a thickness of 12 µm, a dielectric constant of 3.5 and a tangent loss of 0.0027. Both the patch and the ground plane are constructed from copper, ensuring robust performance. The antenna achieves resonance at 5.45 THz with a gain of 14 dB and a bandwidth of 0.7 THz and at 6.34 THz with a gain of 14.44 dB and a bandwidth of 1.77 THz. Additionally, it demonstrates a minor peak at 7.4 THz and a maximum efficiency of 95.87%. The transmission coefficient shows an isolation of -31.01 dB, indicating excellent separation between antenna elements. Key MIMO performance metrics, containing the envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), total active reflection coefficient (TARC), and channel capacity loss (CCL), were analyzed, displaying optimum performance. An analogous circuit was designed and simulated in advanced design system (ADS) to validate these discoveries, creating comparable reflection coefficients to those attained from computer simulation technology (CST) simulations. These findings approve the antenna’s possible for THz-band 6G wireless communication applications.
Volume: 23
Issue: 2
Page: 295-305
Publish at: 2025-04-01

A survey on novel approach to semantic computing for domain specific multi-lingual man-machine interaction

10.11591/ijict.v14i1.pp1-10
Anjali Bohra , Nemi Chand Barwar
Natural language processing (NLP) helps computational linguists to understand, process, and extract information from natural languages. Linguist Panini signifies ’information coding’ in a language and explains that Karakas are semanticosyntactic relations between nouns and verbs that resemble participant roles of modern case grammar. Computational grammar maps vibhakti (inflections) of nominals and verbs to their participant roles. Karaka’s theory extracts semantic roles in the sentences which act as intermediate steps for various NLP tasks. The survey shows that NLP seeks to bridge the gap for man-machine interaction. The work presents the impact of machine learning on natural language processing with changing trends from traditional to modern scenarios with Panini’s classification scheme for semantic computing facilitating machine understanding. The study presents the significance of Karaka for semantic computing, methodologies for extracting semantic roles, and analysis of various deep learning-based language processing systems for applications like question answering. The survey covered around 50 research articles and 21 Karaka-based NLP systems performing multiple tasks like machine translation, question-answering systems, and text summaries using machine learning tools and frameworks. The work includes surveys from renowned journals, books, and relevant conferences, as well as descriptions of the latest trends and technologies in the machine learning domain.
Volume: 14
Issue: 1
Page: 1-10
Publish at: 2025-04-01

A proposed approach for plagiarism detection in Myanmar Unicode text

10.11591/ijai.v14.i2.pp1616-1624
Sun Thurain Moe , Khin Mar Soe , Than Than Nwe
Around the world, with technology that improves over time, almost everyone can access the internet easily and quickly. With the increase in the use of the internet, the plagiarism of information that is easily available on the internet has also increased. Such plagiarism seriously undermines originality and ethical principles. In order to prevent these incidents, there is plagiarism detection software for many countries and languages, but there is no plagiarism detection software for the Myanmar language yet. In an attempt to fill that gap, this study proposed a deep learning model with Rabin-Karp hash code and Word2vec model and built a plagiarism detection system. Our deep learning model was trained by randomly obtaining information from Myanmar Wikipedia. According to the experiments, our proposed model can effectively detect plagiarism of educational content and information from Myanmar Wikipedia. Moreover, it is possible to distinguish plagiarized texts by rearranging words or substituting words with some synonyms. This study contributes to a broader understanding of the complexities of plagiarism in the Myanmar academic area and highlights the importance of measures to effectively prevent plagiarism. It maintains the credibility of education and promotes a culture that values originality and intellectual integrity.
Volume: 14
Issue: 2
Page: 1616-1624
Publish at: 2025-04-01

Strategic Deployment of EV Charging Infrastructure: An In-Depth Exploration of Optimal Location Selection and CC-CV Charging Strategies

10.11591/ijict.v14i1.pp259-267
Debani Prasad Mishra , Pranav Swaroop Nayak , Aman Kumar , Surender Reddy Salkuti
The continued expansion of the electric vehicle (EV) market necessitates strategic planning for the placement of charging stations to ensure efficient access and utilization of electric infrastructure. This paper presents a comprehensive review of the critical factors in optimizing the selection of EV charging station locations, along with the implementation of Constant Current-Constant Voltage (CC-CV) charging models. The study addresses the challenges and opportunities in identifying the most effective locations for charging stations to accommodate the growing demand for sustainable transportation. Furthermore, it examines the benefits of adopting CC-CV charging models to improve the charging process, achieving a balance between charging speed and battery longevity. Through this analysis, the review aims to provide valuable insights to stakeholders involved in the development and expansion of EV charging infrastructure, thereby supporting the transition to a more sustainable and extensive electric mobility ecosystem.
Volume: 14
Issue: 1
Page: 259-267
Publish at: 2025-04-01
1 2 3
...
1995 1996
Show 231 of 1996

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration