Fusion: Practice and Applications

Journal DOI

https://doi.org/10.54216/FPA

Submit Your Paper

2692-4048ISSN (Online) 2770-0070ISSN (Print)

Ultrasound Image Noise Reduction and Enhancement Model based on Yellow Saddle Goatfish Optimization Algorithm

Anamika Goel , Jawed Wasim , Prabhat Kumar Srivastava , Aditi Sharma

In the modern-day diagnostics, ultrasound play an important role in different applications such as vascular, gynecological, cardiac, and obstetrical for diagnosis the various diseases. The main benefit of the ultrasound is that it is non-invasive method and inexpensive. However, in the real-scenario, ultrasound images contain speckle noise which negatively impact the image quality in terms of edges, texture information, and boundaries. In order to eliminate noise, various filters are deployed by researchers in the literature. The limitations of their method are that a fixed level of noise is removed using conventional filters in which parameter values of the filters are fixed. However, in the real-time situation, the noise is random and adaptive filters are required which eliminate any level of noise. To achieve this goal, this paper proposes an adaptive filtering model for eliminate speckle noise based on yellow saddle goatfish optimization (YSGO) algorithm. The YSGO algorithm is based on the hunting behaviour of the fishes. In the proposed model, bilateral filter and speckle-reducing anisotropic diffusion filtering methods and enhancement power law method are taken under consideration. Further, the parameter values of the filtering method and enhancement methods are determined using the nature-inspired YSGO algorithm. The YSGO algorithm minimize the noise and enhances the image brightness and edge information based on the objective function. In our model, mean square error (MSE) and entropy is taken as the objective function. Further, the proposed model is applied on the standard ultrasound images. The visual analysis of the images is done based on the subjective analysis whereas various performance metrics are measured for measure the image quality in the objective analysis. The results reveals that the proposed model outperforms over the existing models in terms of PSNR.

Read More

Doi: https://doi.org/10.54216/FPA.120201

Vol. 12 Issue. 2 PP. 08-18, (2023)

Multimedia Imaging System of Data Collection and Antenna Alignment for Unmanned Aerial Vehicles Based Internet of Things

Maysaloon abed qasim , Qusay Abboodi Ali , Noor Mezher Sahab , Refed Adnan Jaleel , Musaddak Maher Abdul Zahra

Because network of sensors gives a more accurate representation of remotely sensed environments, a network of wirelessly connected sensors is essential. Data packets must be routed to the base station hop by hop, which causes conventional network data collecting to use a lot of power. Unmanned aerial vehicles (UAV) were employed for hovering over the detected environment and gather data to solve this issue. The paper also aims to provide an automatic alignment for UAV antennas for tracking by utilising computer vision technologies. A directional antenna with high gain is used by a ground station that can operate by a pan-tilt to point towards the low-gain omnidirectional antenna carried by the UAV. To center the UAV's antenna's image in the frame, the antenna is equipped with a camera, and a computer detects the video and controls the pan-tilt. The antennas are aligned if there are no more than a few pixels between the UAV image center and the image center. The proposed imaging system exhibits fast data collection, thus attaining a high packet delivery rate and the minimum use of energy. With the suggested antenna auto-alignment approach, the antennas can be accurately aligned with an angle error of under one. UAVs must take the smoothest and shortest pathways possible to accommodate their motion and time constraints. As a result, the Traveling Sales Problem (TSP) is utilized to determine the shortest route, and Bezier curves are then employed to turn paths into a flyable path.

Read More

Doi: https://doi.org/10.54216/FPA.120202

Vol. 12 Issue. 2 PP. 19-27, (2023)

Blockchain and 1D-CNN based IoTs for securing and classifying of PCG sound signal data

Zainab N. Al-Qudsy , Zainab Mahmood Fadhil , Refed Adnan Jaleel , Musaddak Maher Abdul Zahra

The Internet of Things (IoTs) has accelerated with the introduction of powerful biomedical sensors, telemedicine services and population ageing are concerns that can be solved by smart healthcare systems. However, the security of medical signal data that collected from sensors of IoTs technology, while it is being transmitted over public channels has grown to be a serious problem that has limited the adoption of intelligent healthcare systems. This suggests using the technology of blockchain to create a safe and reliable heart sound signal (PCG) that can communicate with wireless body area networks. The security plan offers a totally dependable and safe environment for every data flowing from the back end to front-end. Also in this paper, to classify heart sound signals, we suggested a one-dimensional convolutional neural network (1D-CNN) model. The denoising autoencoder extracted the heart sounds' deep features as an input feature of 1D-CNN. To extract the detailed characteristics from the PCG signals, a Data Denoising Auto Encoder (DDAE) was used instead of the standard MFCC, the suggested model shows significant improvement. The system's benefits include a less difficult encryption algorithm and a more capable and effective blockchain-based data transfer and storage system.  

Read More

Doi: https://doi.org/10.54216/FPA.120203

Vol. 12 Issue. 2 PP. 28-41, (2023)

Blockchain based Certificate Validation

Rachna Jain , Geetika Dhand , Kavita Sheoran , Shaily Malik , Nishtha Jatana

Certificate management is a tedious task for any university or any other organization. These schemes impose problems in Public Key Infrastructure (PKI). Checking the validity and preserving the security of these documents is of utmost importance. In this work, we have devised a blockchain-based solution for preventing malfunctioning in certificate validation which is an important step for any university. Each certificate is uploaded in its hash format and is stored using blockchain. The hashes are stored in unique transactions in nodes, which are deployed on a private network. Using the SHA-256 hashing algorithm, the certificates are uploaded into the system and can be viewed by anyone with the right credentials. Due to the usage of blockchain technology, the certificates are stored in a decentralized manner, which ensures there is no central point of failure. Any changes in the uploaded document need to be validated by other nodes. This paper also improvises that when certificate uploading is required new nodes are added, instead of modifying the past blocks. This work provides a very user-friendly app where any user with the right credentials can upload documents. In this work, digitized documents are stored using Inter Planetary File System (IPFS) which is distributed method of storage. Our theoretical analysis proves that it is a user-friendly application with the security of blockchain technology in partnership with IPFS. Only the issuer can upload documents and others can only view them. Using our proposed solution, problem of malicious certificates can be tackled with E-certification. The proposed method solves all the issues of storing, validating, and sharing documents. Chaotic Map technique is used in hash generation which is quite simple to implement. The proposed approach Chaotic Key based Certificate validation (CK-Cert) provides a hassle-free solution for certificate managements since it better manages the block size as compared to previously proposed techniques (PBCert and CertChain) as discussed with the help of graphs.

Read More

Doi: https://doi.org/10.54216/FPA.120204

Vol. 12 Issue. 2 PP. 42-53, (2023)

Fusion-based Diversified Model for Internet of Vehicles: Leveraging Artificial Intelligence in Cloud Computing

Hayder Sabah Salih , Fatema Akbar Mohamed

The Internet of Vehicles (IoV) is a distributed system that enables data connectivity between vehicles and vehicular ad hoc networks, ensuring efficient and secure information exchange with infrastructures. Challenges in IoV include security clustering related to packet loss during data exchange, real-time analysis of public communication, and the need for autonomous-vehicle technology development using machine learning (ML). ML-assisted IoV has made significant progress in communication with public networks and interaction with the immediate surroundings. This study presents an experimental foundation for the advancement of the IoV system. While support vector machine (SVM) offers a robust and accurate approach for clustering velocity and solving classification challenges related to security, it is primarily a binary classifier and faces limitations in handling multi-class classification. To address this, an artificial neural network (ANN) is proposed for effective packet loss management in the autonomous system, improving the physical layer's secure network and offering better packet loss experience using the Global Positioning System. The fusion-based diversified model not only enables IoV systems to compete with rivals but also provides key advantages to ensure consistent profitability in cloud-enabled IoV. This paradigm integrates cloud computing (CC) with in-vehicle networks and the Internet of Things, offering safety and infotainment applications for road users. Data collection and experiments are conducted using Network Simulator 2 to automate AI configuration in the IoV fusion system.

Read More

Doi: https://doi.org/10.54216/FPA.120205

Vol. 12 Issue. 2 PP. 54-69, (2023)

A Smart Architecture Leveraging Fog Computing Fusion and Ensemble Learning for Prediction of Gestational Diabetes

Zeena N. Al-kateeb , Dhuha Basheer Abdullah

Gestational diabetes (GD) is a growing global concern, underscoring the need for early detection and effective management to prevent adverse health consequences. This paper presents an innovative and reliable architecture to predict gestational diabetes in pregnant women. While reducing the frequency of doctor visits by sending the necessary data via Internet of Things (IoT) technology and receiving the prediction results via a mobile application in real time. The proposed architecture is a fusion of fog computing hardware with ensemble machine learning to enable low-latency, energy-efficient solutions for data processing, and cloud computing. The GD_Fog architecture leverages fused fog computing and load balancing techniques to reduce latency, power consumption, Network bandwidth consumption, and response time, and cloud computing is used based on the concept of use on demand for more reliability while harnessing the power of group learning to improve prediction accuracy. In addition, GD_Fog can be configured for different operating modes to ensure optimal quality of service and prediction accuracy in various fog calculation scenarios, which meet different user requirements. Through extensive testing using real-world data from pregnant women, the framework shows promising results, outperforming the latest methods in accuracy and efficiency. Where the percentage of improvement in prediction accuracy was approximately 6.5% when using ensemble learning, and the improvement in energy use, amounted to approximately 87.01% when using fused fog computing instead of cloud computing. These results confirm the potential of the proposed structure as an invaluable tool for the early detection and effective management of gestational diabetes.

Read More

Doi: https://doi.org/10.54216/FPA.120206

Vol. 12 Issue. 2 PP. 70-87, (2023)