A Review of Online Signature Recognition system

Ibtisam Ghazi; Sharifah Mumtazah Syed; Syamsiah Bt.; Marsyita Bt.

doi:https://doi.org/10.54216/FPA.180111

Fusion: Practice and Applications | 2025

Using Robotic Arm as Sidekick to the Teacher in Classroom

The lack of practical teaching tools, such as a robotic arm, hinders students' understanding of complex concepts in robotics courses, where hands-on experience is essential for effective learning. This study introduced a 6DOF Robotic Arm as a teaching aid to address this issue, evaluating its impact through an experimental study with 30 computer science students. The findings revealed that the robotic arm effectively enhanced both basic and advanced Arduino programming skills, with students who used it performing better and expressing higher satisfaction than those who did not. The study also identified gaps in hardware control comprehension, leading to software development that could further aid in mastering programming concepts. The paper concludes with a discussion of the potential of the robotic arm as a valuable educational tool and its implications for future research and practical applications.

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Mona Esmat mail -

Amira Atta mail

link https://doi.org/10.54216/FPA.180102

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

CORRECTED VERSION: Integrating a Secure and Low-Cost WSN Layer with Medical Cloud Computing for Medical Image Transmission

Throughout a Wireless Sensor Network (WSN), information collected from the environment is continuously transmitted from one node to the next, and then the main collector or server receives and processes it. With the growth of a network, data transfers within the network also grow dramatically. Medical images increase traffic on a network if they are transmitted. An interlayer transmission protocol (WSN) was developed for this study. Pixels are used to create the medical image using the protocol. A gray-level medical image with 512x512 pixels provided by Brain was used to conduct the study. Medical image size is reduced from 256 KB to 192 KB, providing a 25% advantage. A study found SSIM of 51, 1365 and PSNR of 0,9976 for the structural similarity ratio (SSIM). The Advanced Encryption Standard (AES) encryption algorithm safeguards data during the transfer. By creating such a layer, transmissions became safer. In the WSNs, 12.5% and 25% of the data transfer has been reduced based on the information obtained from the study without changing the medical image.

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Israa Hussain Abd Alla mail -

Falath M.Mohammed mail -

Saif Al-din M. N mail -

Azmi Shawkat Abdulbaqi mail

link https://doi.org/10.54216/FPA.180103

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Machine Learning for Link Prediction between Nodes in Complex Networks

Recently, the complex network has become popular use as it can transfer huge amounts of multimedia, text, ideas, and other information, encouraging many participant connections. Social media is one of these networks that make the most connections. Predicting the formation or dissolution of links between nodes presents a problem for social network analysis researchers. Since social networks are dynamic, this task is exciting as it may also forecast lost network links with less information. On the other way, current link prediction methods use simply node similarity to find links. This study proposes a new technique that relies on node attributes and similarity measures. Nodes are labeled by their centrality and similarity. The network's edges are negative and positive samples. A well-defined dataset for link prediction comprises the features of the nodes at the edges labeled either positive or negative. The dataset is passed to multiple machine learning classifiers. On several real-world networks. The experiments conducted during the research show that Gradient Boosting gave the highest accuracy of 99% compared with other methods.

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Elaf Adel Abbas mail -

Nisreen Abbas Hussein mail -

Raaid Alubady mail

link https://doi.org/10.54216/FPA.180104

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Medical Assistant System for Athletes' Health Analysis Based on EMG-Signals Activity and Virtual Instruments as a Step towards the Internet of Medical Things

Monitoring and analyzing athletes' jumps system using Electromyography (EMG) signals based on Virtual Instruments (LabVIEW) is presented in this paper. This system was prototyped using the virtual instrument workbench (LabVIEW) to display the jumping pattern. In Jump analysis hardware (JA-H/W), there are sensory boards, ultrasonics, and wireless communication systems. To measure the minimum foot clearance (MFC) and orientation, there have been two types of systems used to simulate Jump Analysis Software Ultrasonic (JAS-UltSnc) as well as Inertial Measurement Unit (JAS-IntMeUnt). Combining JAS-UltSnc with JAS-IntMeUnt provided a complete solution with error correction. LabVIEW is used to display the jump patterns generated by the system and analyze the jump patterns of the athlete.

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Radwan Nazar Hamed mail -

Mohannad Al-Kubaisi mail -

Alyaa Hashem Mohammed mail -

Azmi Shawkat Abdulbaqi mail

link https://doi.org/10.54216/FPA.180105

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

CORRECTED VERSION: A System of Human Biometric-Fusion Authentication Security Improvement Using Hybrid Technique

The collected information from the environment in WSN continuously sends from one node to another until it reaches the main collector or server, where processing is done. The transferred data volume will be greater when the network grows. Medical images will also contribute to network traffic. To alleviate this challenge, this research has developed an interlayer transmission protocol for WSNs. This protocol uses the construction of medical images with pixel-based data. In the analysis, a gray-scale medical image 512x512 in size, provided by Brain, is utilized. The image was compressed by the protocol from 256 KB to 192 KB with a percentage of 25%. As a result, the structural similarity index measure showed the SSIM at 51.1365, while the PSNR is at 0.9976; therefore, the quality of the medical image remains unchanged. The protocol uses the AES encryption method for strong data protection to improve security during transmission. Results show that this protocol reduces data transmission in WSNs by 12.5 to 25% without affecting the integrity of the medical image, which is indicative of the efficiency of the protocol in enhancing network performance while ensuring data safety.

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Salwa Mohammed Nejrs mail -

Azmi Shawkat Abdulbaqi mail

link https://doi.org/10.54216/FPA.180106

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

The Imperative Necessity of Erbil-Koya Highway Stretch

Human civilization encompasses all that humans have created, both materially and morally, within a specific time and place. Thus, building highway extensions represents a significant addition to the material aspects of civilization. Highways are a crucial component of human development, affecting societies in social, economic, environmental, urban, and cultural ways. Connecting Erbil with Koya via a highway is expected to affect the populations of both cities and their surrounding areas. This paper examines the role of highways in societal development, with a particular focus on Koya. We have demonstrated the importance of highway design through mathematical models using modern speed parameters, fuzzy logic, and control methods. Additionally, we proposed a method for managing highway speeds through radar and remote sensing technologies. The paper highlights the inevitable societal progress resulting from the Koya-Erbil highway connection.

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Abdulqader Othman Hamadameen mail

link https://doi.org/10.54216/FPA.180107

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Real-Time Electric Vehicle Battery SOC Estimation Using Advanced Optimization Filtering Techniques

Improving the Extended Kalman Filter's (EKF) State of Charge (SOC) prediction for EV battery packs is the primary goal of this section. Optimised batteries management procedures rely on SOC estimate that is both accurate and reliable. The EKF is a popular tool for estimating nonlinear states, but how well it works relies heavily on which noise coefficient matrices are used (Q and R). Experimental testing and other conventional approaches of calibrating these matrix systems are extremely costly and time-consuming. In order to tackle this, the section delves into the integration of four state-of-the-art metaheuristic optimisation methods: GA, PSO, SFO, and HHO. By minimising the mean square error (MSE) among the real and expected SOC, these techniques optimise the Q and R matrices. When looking at preciseness, converging speed, and resilience, SFO-EKF comes out on top in both static and dynamic comparisons. By greatly improving the reliability of SOC estimations, the numerical results show that SFO-EKF obtains the lowest MSE & RMSE. This study advances electric car batteries by providing a realistic scheme for combining optimisation methods with EKF to offer highly effective and exact SOC estimates. When as opposed to TR-EKF, GA-EKF, PSO-EKF, and HHO-EKF, the SFO-EKF approach shows the best accuracy, with an improvement of over 94%. This is a result of the suggested model's exceptional efficiency in SOC estimates.

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Hari Prasad Bhupathi mail -

Srikiran Chinta mail -

Vijayalaxmi Biradar mail -

Sanjay Kumar Suman mail

link https://doi.org/10.54216/FPA.180108

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Efficient Data Processing Techniques for Structured Data Analysis Using Stream Pipeline Parallelism

 This research illustrates how dynamic task balancing and data sharing may improve distributed data processing. The technology handles parallel processing system difficulties with huge datasets by minimizing resource utilization, time complexity, and output. We modify the workload on the fly after splitting to ensure that all processing units receive equal work. One last optimization phase optimizes job distribution to maximize system efficiency. We test the solution for latency, speed, scalability, resource utilization, fault tolerance, and synchronization overhead. Results reveal that the new strategy outperforms existing ones in every regard. It features the lowest latency, quickest production, and highest growth potential. The approach handles mistakes well, divides data effectively, and syncs everything at a cheap cost. These properties make it ideal for real-time data processing and fast-growing applications. Future study will concentrate on flexible splitting strategies, fault tolerance mechanisms, and predictive analytics machine learning models. These modifications will improve real-time data handling.

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Sampath Kini K. mail -

D. K. Sreekantha mail

link https://doi.org/10.54216/FPA.180109

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Sentiment Analysis on Amazon Reviews of Mobile Phones using Machine Learning

The world is witnessing a boom in the digital age. Digital shops have literally landed into our homes. Almost any required product can now be purchased online via websites or mobile apps without having to step out. Due to online shopping, many customers rely on online reviews from other customers before making a purchase. Customer reviews are gaining more and more importance as they play a probably vital role in the sale and purchase of a product. Customer reviews also provide firsthand feedback coming directly from the customers themselves; this can benefit even the sellers in improving future sales. Analyzing the reviews can provide probable causes for failure or success of a product. Henceforth, the current paper presents the sentiment analysis of the reviews to better understand the feelings expressed by the customers. The very popular and widely used mobile phones were chosen as the product and Amazon was chosen as the digital seller for the current study. Initially, this work began with data preprocessing. Followed by data preprocessing, Bow and n-grams word embedding have been used to represent the clean reviews in vector representation, and then the features were derived. Finally, the performance of supervised machine learning classifiers such as Decision Tree, Naive Bayes, Random Forest, and SVM was empirically evaluated through accuracy, recall, f1-score, and precision. The results of empirical evaluation revealed that the Random Forest Classifier shows best performance with 97.48% accuracy.

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Shweta Singhal mail -

Huda Lafta Majeed mail -

Hassan Muayad Ibrahim mail -

Nishtha Jatana mail -

Charu Gupta mail -

Agam Kumar mail -

Bharti Suri mail -

Oday Ali Hassen mail

link https://doi.org/10.54216/FPA.180110

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

A Review of Online Signature Recognition system

Biometrics has reached an important place in the field of authentication for both financial transactions and document verification. Signatures can be broadly classified into online and offline types, depending on how they are acquired. Captured through devices like tablets and digital pens, online signatures contain rich features concerning position, velocity, and acceleration; hence, they offer a better resistance to forgery compared to offline, more traditionally taken signatures. The review summarized the current research in online signature verification systems. There are methodologies and techniques deployed for feature extraction, data pre-processing, and classification. The main stages reviewed within the verification process are about data acquisition, including the use of several publicly available databases like DEEPSIGN, SVC2004 and MCYT-100. Wavelet transforms and Fourier analysis are discussed as a number of methods employed for feature extraction, showing good results about signature dynamics. This review follows the SLR approach for analysing and synthesizing relevant studies published between 2017 and 2024. This review uses PRISMA guidelines for the selection of studies, hence making the results methodologically rigorous and unbiased. The paper identifies commonly used algorithms, including CNN, RNN, and DTW, and examines popular signature databases by outlining their characteristics and relevance to system performance. The insights from this review will help in pointing towards the future ahead in online signature verification systems through emphasizing deep learning-based techniques along with realistic challenges.

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Ibtisam Ghazi Nsaif mail -

Sharifah Mumtazah Syed Ahmad mail -

Syamsiah Bt. Mashohor mail -

Marsyita Bt. Hanafi mail

link https://doi.org/10.54216/FPA.180111

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new