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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Ahmed Dheyaa Radhi
University of Al-Ameed
Computer Science & IT Electrical & Electronics Engineering

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Design module for speech recognition graphical user interface browser to supports the web speech applications Fadya A. Habeeb; Suaad M. Saber; Shaymaa Mohammed Abdulameer; Hassan Muwafaq Gheni; Ahmed Dheyaa Radhi
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.4346

Abstract

The web speech API has made it possible to integrate audio data into web applications and make it a unique experience for all customers and users of modern applications. The website can only be accessed through devices equipped with a which stands for graphical user interface (GUI) and screen. For this to be done, there must be a physical attraction with such devices. This paper presents speech recognition using a web browser (SRWB) which permits browsing or surfing the internet with the use of a standard voice-only and vocal user interface (VUL) development. The SRWB system input from the users in form of vocal commands and covers these voice commands to HTTP requests. The SRWB system will send the voice commands to the web server for processing purposes and when the processing is done, the converted or translated HTTP response is outputted to the end-users in a voice format made audible with the attached loudspeakers. SAPI, developed by Microsoft, allows the use of SRWB in Windows applications. The algorithm is implemented by the system to achieve its goal for web content, classifying, analyzing, and sending important parts of web pages back to the end-user.
Real time pedestrian and objects detection using enhanced YOLO integrated with learning complexity-aware cascades Ahmed Lateef Khalaf; Mayasa M. Abdulrahman; Israa Ibraheem Al_Barazanchi; Jamal Fadhil Tawfeq; Poh Soon JosephNg; Ahmed Dheyaa Radhi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.24854

Abstract

Numerous technologies and systems, including autonomous vehicles, surveillance systems, and robotic applications, rely on the capability to accurately detect pedestrians to ensure their safety. As the demand for real-time object detection continues to rise, many researchers have dedicated their efforts to developing effective and trustworthy algorithms for pedestrian recognition. By integrating learning complexity-aware cascades with an enhanced you only look once (YOLO) algorithm, the paper presents a real-time system for identifying both items and pedestrians. The performance of the proposed approach is evaluated using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) pedestrian dataset across both the v4 and v8 versions of the YOLO framework. Prioritizing both speed and accuracy, the enhanced YOLO algorithm outperforms its baseline counterpart. The demonstrated superiority of the suggested technique on the KITTI pedestrian dataset underscores its effectiveness in real-world contexts. Furthermore, the complexity-aware learning cascades contribute to a streamlined detection model without compromising performance. When applied to scenarios requiring real-time identification of objects and individuals, the proposed method consistently delivers promising outcomes.
5G-backed resilience and quality enhancement in internet of medical things infrastructure for resilient infrastructure Noora Jamal Ali; Noor Amer Hamzah; Ahmed Dheyaa Radhi; Yitong Niu; Poh Soon JosephNg; Jamal Fadhil Tawfeq
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.24863

Abstract

The internet of medical things (IoMT) has transformed the healthcare sector by facilitating real-time monitoring, remote patient care, and tailored healthcare solutions. However, the challenge of upholding a high standard quality of service (QoS) in IoMT implementations remains a pressing issue. This article delves into the possibilities of utilizing 5G networks and smart techniques to optimize QoS within IoMT systems. By capitalizing on the capabilities of 5G networks, including substantial bandwidth, minimal latency, and extensive connectivity, in conjunction with intelligent methods such as machine learning and predictive analytics, this paper introduces novel strategies to enhance QoS in IoMT environments. The summary underscores the advantages of these methods in elevating network dependability, diminishing latency, enhancing data transmission efficiency, and enabling resource allocation efficiency in IoMT deployments. Additionally, it explores the potential ramifications of these developments on healthcare outcomes, patient contentment, and the overall effectiveness of the healthcare system. The conclusions propose that by maximizing QoS through 5G networks and intelligent techniques, IoMT holds the potential to significantly enhance the delivery of healthcare services, fostering a more interconnected and efficient healthcare ecosystem in the process innovation.
Detecting community on social networks with fast and optimal online clustering algorithms Muneer Sameer Gheni Mansoor; Hasanain Abdalridha Abed Alshadoodee; Rahim Muhammad Alabdali; Ahmed Dheyaa Radhi; Poh Soon JosephNg; Jamal Fadhil Tawfeq
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 2: April 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i2.24724

Abstract

Social networks have become an essential part of our lives today, at least in their virtual dimension, and the image of the web world is almost impossible without the presence of this pervasive phenomenon. These networks are one of the important components of the information infrastructure, such as twitter networks, facebook networks, and so on. In the analysis of social networks, one of the important issues is the detection of community. Each community is a group of network nodes so that the connection between nodes within the group with each other is more than their connection with other network nodes. Various methods have been proposed for community detection. One of the existing methods is based on data stream clustering. The output data of a social network can be modeled with a data stream. Fast and accurate clustering of this data stream can be very effective in the detection of community. In this research, using a fast and accurate online clustering algorithm, the community is detected. The simulation results indicate that the method proposed in this research can calculate the number of clusters optimally and perform better than similar methods. The proposed algorithm can be used in many other applications.
A powerful heuristic method for generating efficient database systems Haider Hadi Abbas; Poh Soon JosephNg; Ahmed Lateef Khalaf; Jamal Fadhil Tawfeq; Ahmed Dheyaa Radhi
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5070

Abstract

Heuristic functions are an integral part of MapReduce software, both in Apache Hadoop and Spark. If the heuristic function performs badly, the load in the reduce part will not be balanced and access times spike. To investigate this problem closer, we run an optimal database program with numerous different heuristic functions on database. We will leverage the Amazon elastic MapReduce framework. The paper investigates on general purpose, implementation, and evaluation of heuristic algorithm for generating optimal database system, checksum, and special heuristic functions. With the analysis, we present the corresponding runtime results. For the coding part, the records counting part is hasty and can only work for local Hadoop part, it can be debugged and optimized for general purpose implement on Hadoop and Spark and turn into an effective performance monitor tool. As mentioned before, there are strange issue, also the performance of BLAKE2s is unexpectedly slow in that it’s widely accepted the performance of BLAKE2s is much better than MD5 and SHA256, we would like to figure out why the common-sense performance of heuristics is deferent from what we got in distributed frameworks.
Co-Authors Ahmed Lateef Khalaf Ahmed Lateef Khalaf Fadya A. Habeeb Haider Hadi Abbas Hasanain Abdalridha Abed Alshadoodee Hassan Muwafaq Gheni Israa Ibraheem Al_Barazanchi Jamal Fadhil Tawfeq Mayasa M. Abdulrahman Muneer Sameer Gheni Mansoor Noor Amer Hamzah Noora Jamal Ali Poh Soon JosephNg Rahim Muhammad Alabdali Shaymaa Mohammed Abdulameer Suaad M. Saber Yitong Niu