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International Journal of Informatics and Communication Technology (IJ-ICT)
Published by Institute of Advanced Engineering and Science
ISSN : 22528776     EISSN : 27222616     DOI : -
Core Subject : Science,
Computer Science & IT
International Journal of Informatics and Communication Technology (IJ-ICT) is a common platform for publishing quality research paper as well as other intellectual outputs. This Journal is published by Institute of Advanced Engineering and Science (IAES) whose aims is to promote the dissemination of scientific knowledge and technology on the Information and Communication Technology areas, in front of international audience of scientific community, to encourage the progress and innovation of the technology for human life and also to be a best platform for proliferation of ideas and thought for all scientists, regardless of their locations or nationalities. The journal covers all areas of Informatics and Communication Technology (ICT) focuses on integrating hardware and software solutions for the storage, retrieval, sharing and manipulation management, analysis, visualization, interpretation and it applications for human services programs and practices, publishing refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in ICT.
Arjuna Subject : -
Articles 650 Documents
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Enhanced thermal management in 3D integrated circuits coupling Vempalle Rafi; Shaik Hussain Vali; Pradyumna Kumar Dhal; Sadhu Radha Krishna; Murkur Rajesh; Malagonda Siva Kumar
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1208-1216

Abstract

3D IC integration, which comprises vertically stacking several IC layers, is one of the new technologies that works well with complementary metal-oxide-semiconductor (CMOS) implementations. The layers of a three-dimensional integrated circuit (3D IC) are physically and electrically connected via copper-silicon bonding and through silicon vias (TSVs). Limitations in 3D IC designs, such as layer-to-layer thermal difficulties and TSV-to-substrate and TSV-to-TSV noise coupling, significantly impact system performance as a whole. Integrating 3D ICs relies heavily on heat spreaders and thermal through silicon vias (TTSVs). Overheating is a common cause of IC failure; however, heat spreaders and FIN to TTSV have been suggested as potential remedies for this problem in the last few years. A 3D IC might melt under the stress of an applied voltage because it becomes hotter inside. Engineers have added fins to the TTSV in a number of ways, each of which maximizes heat dissipation in a different way, in order to reduce this danger. The exceptional thermal cooling characteristics of graphene and carbon nanotubes (CNTs) have led to their widespread dissemination. This research shows that a FIN may efficiently transport thermal energy to a heat sink by using heat spreaders and optimum orientations to distribute heat in all directions. Additionally, we demonstrated the many scenarios in which the IC's potential distribution is impacted by various thermal cooling effects. We found that when it comes to transferring heat away from heat sources and TSVs, CNTs outperform Graphene. We included Al2o3, Si3N4, and SiO2 as examples to examine the consequences of modifying the model's dielectric characteristics.
A blockchain-enabled edge–cloud framework for secure, scalable IoT data sharing Peda Narayana Bathula; Sadhu Radha Krishna; Meruva Sreenivasulu
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1777-1790

Abstract

Hybrid edge exchange chain (HybrEx-chain) is a blockchain-enabled hybrid edge–cloud framework for secure and scalable data sharing in internet of things (IoT) networks. The framework combines edge computing for fast local pro cessing with cloud computing for resource-intensive tasks and global coordina tion. Proxy re-encryption (PRE) based on elliptic curve cryptography (ECC) protects data end-to-end and enables confidential data sharing without exposure of plaintext at intermediate nodes. A hierarchical consensus mechanism inte grates delegated practical byzantine fault tolerance (d-PBFT) at edge clusters with delegated proof of stake (DPoS) at the cloud layer to improve scalability and reduce consensus delay. Smart contracts on the blockchain manage time based re-encryption key rotation and enforce adaptive access policies to support automated security management. The framework is evaluated in a simulation environment based on Urban Deployment, Industrial IoT, and Smart Healthcare scenarios. The results show lower latency (140.5–155 ms), higher throughput (1150–1255 transactions per second), high scalability (up to 99%), and reduced consensus time compared with existing PRE-based frameworks FE-PRE and PREA. By combining edge responsiveness, cloud-level scalability, and cryptographic key management, HybrEx-chain provides an effective approach for secure and efficient IoT data sharing.
DDoS attack detection on portmap using a bidirectional recurrent neural network method Wisnu Adi Putra; Deris Stiawan; Qurhanul Rizqie; Nurul Afifah; Septiani Kusuma Ningrum; Rahmat Budiarto
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1601-1613

Abstract

Portmap is a protocol used in Unix and Linux operating systems to map port numbers to their associated program numbers. In a distributed denial of service (DDoS) attack on portmap, attackers will exploit a weakness in this protocol to send many malicious or manipulative server-running portmap requests Due to the connectionless and unauthenticated nature of UDP, the portmap server will attempt to respond to every request it receives. However, attackers often disguise this so that their IP address is hidden or spoofed. As a result, the server will attempt to reply to requests to a spoofed or invalid IP address, using server resources and causing a denial of service for valid users. A DDoS attack on portmap can be quite damaging. Services associated with portmap may become unresponsive, causing significant downtime for applications or services that rely on the protocol. This study uses the CSE-CIC-IDS 2019 dataset, especially the DDoS attack dataset on portmap. This paper also employs the correlation-based feature selection (CSF) approach to extract significant characteristics from the dataset Bidirectional RNN is used to detect DDoS assaults. Utilizing 30% testing data and 70% training data, the Bidirectional RNN approach can effectively identify DDoS attacks on portmap. Accuracy is 99.9917%, recall is 99.9917%, specificity is 99.9942%, precision is 99.994%, F1-Score is 99.88%, balanced accuracy (BACC) is 99.92%, and matthews correlation coefficient (MCC) is 99.98%. By merging the CFS feature selection technique with the Bidirectional RNN architecture, this work mainly contributes to creating a more accurate and efficient DDoS attack detection method. With this method, the detection process can be carried out faster and more precise, reducing the computational burden without sacrificing detection performance.
Developing a machine learning-based algorithm for secure routing in autonomous vehicles Shwetansh Priyadarshi; Alok Srivastava
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1746-1758

Abstract

Over the last several years, we have noticed that the administration and car companies have been refraining from investing capital in connected autonomous cars. A chain of interconnected sensors, actuators, and devices—combining lidar, GPS, radar, in-system machines, cameras, and more—allows autonomous cars to learn how to operate in real-time scenarios. However, data from recent cyberattacks and mishaps suggests that malicious attackers can access this sensor data or even the main control system through hacking, leading to a variety of losses. As time passes by, people will be less interested in connected autonomous vehicles (CAVs), and various surveys say that it will be mainly because of the absence of a proper security infrastructure. A study of cyberattacks on CAVs and strategies to eliminate them is discussed in this study. In this study, we have proposed a hybrid model solution that uses convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), and we are optimizing the Hyperparameters using the dwarf mongoose optimization algorithm (DMOA). The proposed model is also tested in Comparison with other existing models, and we are able to detect DoS, fuzzy, spoofing the driving gear, and spoofing the RPM gauge attacks with an accuracy of more than 98%.
Smart integrated system for optimal resource utilisation Rama Krishna Peddarapu; Vaishnavi Chillara; Sudha Manasa Jagilanka; Maneesh Kanumolu; Manvitha Choutymetla
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1921-1928

Abstract

One of the main obstacles to agriculture development and sustainability is the improper use of such natural resources as water, fertilizer, and energy. In this paper, the concept of smart integrated system for optimal resource utilization (SISORU) will be presented. The idea of the project consists of the development of the web application based on ICT, machine learning (ML), and visualizing technology on behalf of the executive process in agriculture. SISORU differs from existing agricultural solutions by combining the recommendations on crop choice, cost estimation, and resource shortages detection in one solution.
A muzzle-based self-enhancing cow recognition method using YOLO and LLM Rifat Sarker Aoyon; Ismail Hossain; JIA UDDIN
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1861-1870

Abstract

In this research, we have presented an approach to muzzle-based cow identifica tion. To recognise muzzles, we have used you only look once (YOLO) and the structural similarity index (SSI). In the proposed method, a large amount of data is not required for the image processing task. Because here we included a self enhancing feature, which helps the model to predict accurately and also helps to improve performance by increasing the amount of data. This certain program helps the model to improve its capability for delivering faster responses. large language model (LLM) is responsible for adding the self-enhancing feature. On the other hand, to find out the most suitable YOLO model for our research, we have trained data with three different versions of YOLO: YOLO v7, v8, and v9. We evaluated the performances of these three versions in terms of Preci sion, Recall, mAP50, and mAP50-95 and found YOLO v9 to be the best among them. It excels with a precision of 0.391, a recall of 0.916, a mAP50 of 0.559, and a mAP50-95 of 0.307. Here, data have been collected manually and were annotated by Roboflow.
5G network performance analysis and planning for the development of 5G services in Padang City Afrizal Yuhanef; Zurnawita Zurnawita; Farhan Sya’banul Alza
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1791-1799

Abstract

The rapid deployment of fifth-generation (5G) mobile networks requires accurate performance assessment to support effective network planning. This study presents a combined field measurement and simulation approach, integrating drive test data with Atoll-based coverage planning to evaluate the early deployment of 5G non-standalone (NSA) in Padang City, Indonesia. Radio performance was assessed using reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR) obtained from drive tests using a Samsung Galaxy A26 5G and G-NetTrack Pro, while Atoll was used to analyze planned coverage. The results show that 64% of measurement points achieved very good RSRP (≥ −85 dBm) and 56% recorded very good SINR (≥ 10 dB), although several locations exhibited poor SINR despite strong RSRP, indicating interference associated with the NSA architecture. Atoll simulation showed that only 36.42% of the planned coverage area reached excellent RSRP, revealing gaps between measured and predicted performance. These findings demonstrate that integrating drive test measurements with propagation simulation provides a practical framework for identifying coverage deficiencies and supporting 5G network planning through interference mitigation, antenna parameter adjustment, and migration toward SA architecture.
Deep transformer architectures for real-time e-payment fraud detection under class imbalance Riad Loukili; Fayçal Messaoudi; Raouya El Youbi; Manal Loukili
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1768-1776

Abstract

E-commerce has become a rapidly expanding channel, leading to more e-commerce payment transactions and an increased threat for advanced fraudulent activities. New frauds include cross-channel attacks, adversarial behavior and synthetic identities, which are challenging to rule-based detection. In this paper, a deep learning framework for detecting e-payment fraud in real-time is proposed. The model integrates self-attention, explainable AI, class balancing using GANs, and a transformer-based architecture to enable transparent decision-making. The solution tackles major issues in fraud detection, such as class imbalance, evolving transaction trends, and the requirement for interpretability in compliance with regulations like the EU AI Act. Experiments on benchmark datasets demonstrate good results, with an accuracy of 99.82%, and an AUC-ROC of 99.56%. Based on the results, the use of transformer-based models with the help of synthetic data generation and explainability can enhance the reliability and transparency of e-payment fraud detection systems.
SmartMob-PPT-CLAMA: Traceable anonymous authentication for V2X mobility Haewon Byeon
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1954-1963

Abstract

Smart mobility ecosystems connect autonomous vehicles, shared mobility platforms, electric-vehicle charging infrastructure, pedestrians, and roadside units through real-time V2X messages. This paper redesigns PPT-CLAMA, a privacy-preserving and traceable certificateless anonymous mutual authentication scheme, for high-speed internet of vehicles (IoV) and smart mobility environments. The proposed SmartMob-PPT-CLAMA introduces mobility-adaptive pseudonym pools, RSU-assisted handover caching, lightweight V2X authentication headers, hierarchical traceability, and Sybil aware regional trust filtering. The protocol preserves the original security objectives of anonymity, unlinkability, perfect forward secrecy, known session-specific temporary information security, and lawful traceability, while addressing mobility-specific weaknesses such as long-lived pseudonym tracking, handover re-authentication delay, centralized trace authority failure, high-speed Sybil attacks, and broadcast replay. In the evaluated Raspberry Pi 4B and SUMO/NS-3 workload, the proposed design reduces authentication delay from 1.10 ms to 0.82 ms, reduces communication overhead from 470 bits to 312 bits, and achieves 99.1% handover re-authentication success. The design is mapped to ETSI ITS security, IEEE 1609.2, ISO/SAE 21434, and GDPR Article 32 requirements.
Arabic calligraphy style recognition with edge enhancement and attention Bilal Abdulrahman Tuama; Farhan Mohamed
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1510-1522

Abstract

Arabic calligraphy style recognition is a challenging task because visually similar styles often share curved strokes, connected structures, and subtle ornamental variations. This study proposes a convolutional neural network (CNN) architecture that combines learnable edge enhancement, squeeze-and excitation (SE) attention, and optional BiLSTM-based sequential modeling for Arabic calligraphy style classification. The edge module is designed to preserve fine-grained stroke boundaries, while the attention mechanism emphasizes informative channel responses and the BiLSTM models width wise contextual dependencies. Experiments were conducted on a public Arabic calligraphy style dataset using a stratified 70/15/15 train-validation test split. The best configuration achieved 98.22% accuracy and 98.20% F1-score on the test subset. These findings indicate that adaptive edge-aware feature learning can improve the discrimination of visually similar Arabic calligraphy styles. Nevertheless, broader validation on additional datasets and repeated runs is still needed to confirm generalizability.

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