Implement Intelligent YOLOv8 for Car Crowd Detection and Counting in the Roads

Noor Abdul Khaleq; Rand A.; Hussein M.; Asmaa S.; Saja B.

doi:https://doi.org/10.54216/JISIoT.160111

Journal of Cybersecurity and Information Management | 2025

Innovative Resilient Systems Scheduling Methods for Explicit Critical Applications in Cloud Environments

The model mentioned in the study introduces a new Puzzle Optimization Algorithm-Based Fault Tolerant Scheduling (POAB-FTS) model specifically designed for the cloud computing setting. This pinpoints the significant challenge of achieving reliability, availability, and performance in resource scheduling in the context of failure cases, which is addressed by this novel technique. The POAB-FTS methodology integrates optimization using a game theory approach to perform actions that reduce execution time and failure probability while using a fitness function to provide better decision-making. This work entails an assessment of the main reasons behind task and hardware failures such as lack of resources, hardware defects, and suboptimal implementation. The model covers both active and passive fault tolerance approaches to workload balancing, migration before failure, and migration after failure points. Cooking schedules derived from the POAB-FTS technique are compared against the MAXMIN, ACO, and GTO-FTASS algorithms to present the makespan, failure ratios, and failure slowdowns—giving a comprehensive comparison of the method. As shown in this paper, the POAB-FTS framework can improve the system’s fault-tolerance and adapt resource allocation based on the actual demand thereby stressing its capacity to act as a scalable and cost-efficient solution for the improvement of cloud computing infrastructures. On this contribution, a sound and optimal cloud resource management is made possible.

groups

Adel A. Alyoubi mail

link https://doi.org/10.54216/JCIM.160107

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Biometric Data Securement Using Visual Information Encryption

Biometric data is becoming increasingly valuable because of its uniqueness, and digital watermarking techniques are used to protect it. This paper presents a new method of hiding Palmprint images using wavelet decomposition and Encrypting Visual Information (EVI). EVI is a technique for securing Palmprint print images that has been extensively studied in this report. By embedding the Palmprint image in the cover image, and then using wavelet transformation, this output image can be decomposed into four segments (Segment Low Low, Segment Low High, Segment High Low, and Segment High High). A compressor is placed at the sender site to compress these four segments. DWT is obtained at the receiver side and then the bit-matching procedure is applied to obtain the original palmprint image. Using data concealing and EVI implementations on biometrics, palmprints, and related textual information can be protected from identity fraud. The watermarked cover images and palmprints, which could be used for authentication, have been improved from the existing approach. By reducing the segment size, quality is achieved along with higher security and bandwidth reduction. In addition, the three least significant bits are successfully applied to increase the length of a secret message while retaining palmprint quality.

groups

Sawsan D. Mahmood mail -

Hadeel M Saleh mail -

Asraa Y. Youssef mail -

Lara Ahmad Ghasab Almashagba mail -

Fathiya Al Abri mail

link https://doi.org/10.54216/JCIM.160108

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

A Novel Algorithm for Optimized Cluster Head Selection in Wireless Sensor Networks

Wireless Sensor Networks are everywhere around us used in variety of applications such as weather forecasting, military surveillance, health monitoring, agriculture monitoring, and smart IoTs etc. These networks are particularly employed to sense and broadcast the data from source nodes to sink node. Hence, energy consumption becomes one of the most challenging jobs here. Hierarchical clustering-based routing schemes prove to be helpful in such situations. As a result, optimized cluster head selection is essential and key task here. In this paper author has attempted to design an optimized cluster head selection scheme based on Adaptive Hybrid Dragonfly Firefly (AHDF) algorithm based on node energy, corresponding distance and network load and delay parameters. The simulation and comparison results showcase the outperformance of the proposed routing scheme in terms of energy efficiency (121% and 41%), network lifetime (89% and 21%) and data throughput (31% and 23%) in comparison of existing routing schemes SEELCA [15] and CRCGA [16] respectively.

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Vani S. Badiger mail -

Ganashree T. S. mail -

Vinod B. Durdi mail -

Srividya B. V. mail -

T. Christy Bobby mail -

Anju V. Kulkarni mail

link https://doi.org/10.54216/FPA.190108

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Improving Video Streaming Quality and Network Efficiency through Data Distribution Services

Lately, handling big data has become challenging due to its large size and complexity. To address scalability, availability, real-time performance, flexibility, and various Quality of Service (QoS) characteristics, Data Distribution Services (DDS) middleware offers extensive integration with mission-critical, real-time, and high-performance networks. Unlike traditional client-server communication models, Data Distribution Services rely on a publish/subscribe communication model. DDS enhances the quality of video streaming through its efficient data delivery approach. On internet protocols, a significant portion of traffic is generated by content delivery applications, such as video streaming. This study examines how Data Distribution Services are well suited for streaming real-time, full-motion videos over communication networks. Several experimental studies have been conducted to compare video streaming using a VLC player with an overlay of Data Distribution Services. Our application-aware routing system enables mobile network operators to utilize their networks more efficiently, allows service providers to improve customer satisfaction, and ensures end-users experience desirable service quality across various network applications. The findings of this study demonstrate the efficiency of DDS in delivering high-quality video streams while utilizing low network bandwidth. Additionally, the results highlight that DDS offers greater flexibility and scalability, making it a highly important technology for video distribution over internet protocol networks. It achieves this by using narrower bandwidth while maintaining high-quality video stream delivery.

groups

Mohammed Q. Jawad mail -

Mohammed Yousif mail

link https://doi.org/10.54216/FPA.190109

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

AI-Driven Cryptographic and Steganographic Integration for Enhanced Text Security Using OpenAI API

Artificial Intelligence (AI) can become a great asset to produce cryptographic keys in order to improve the security of the encryption methods. While using machine learning algorithms AI can generate most complex and unpredictable keys to prevent brute-force and cryptanalyst attacks. Key generation using AI also allows the design of cryptographic solutions that adapt to the context in which the key is used. It also enhances the conventional security measures while simultaneously providing great opportunities for creating flexible security solutions. This paper proposed a new text security method based on the integration of the cryptography and steganography, where the suggested method is done based on OpenAI API. The proposed method is consisted of three steps, and these steps are key generation, text encryption, and data embedding. The first step, is utilized by using GPT-2 model to generate set of keys for both cryptography and steganography steps. The second step, is starting by converting the plaintext to ASCII format, then performed modulo arithmetic operation between ASCII values and the keys that generated from the previous step, then convert the achieved equation results to Hexadecimal format, and finally convert these values to binary and these values represent the final ciphertext. The last step of the proposed method is done by hiding the binary values within image, this done by select positions randomly, then used GPT-2 model to generate another set of keys to shift the values of random positions, then applied least significant bit (LSB) algorithm to hide the bits within the final position with different color channels. The proposed approach provides a basis for the development of new-generation secure communication systems in the context of AI.

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Omar Fitian Rashid mail -

Saba A. Tuama mail -

Imad J. Mohammed mail -

Mohammed Ahmed Subhi mail

link https://doi.org/10.54216/FPA.190110

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Early Cancer Detection: Hybrid Combination of Deep Learning and Computer Vision for Medical Images

Medical imaging performs a critical position in modern healthcare, in particular in the early detection of cancers, which considerably enhances survival charges and treatment consequences. This study investigates a hybrid version combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to optimize medical image analysis. Leveraging advanced deep gaining knowledge of strategies along with Transfer Learning and Data Augmentation, the hybrid method validated advanced performance in class, segmentation, and anomaly detection obligations. Experimental results discovered that the hybrid version outperformed standalone CNN and ViT architectures, attaining high diagnostic accuracy whilst keeping computational efficiency. The findings spotlight the potential of AI-stronger answers to revolutionize clinical diagnostics by way of offering accurate and reliable computerized systems, paving the manner for broader medical programs and improved patient results.

groups

Bushra Majeed Muter mail -

Fatima Hameed Shnan mail -

Huda Lafta Majeed mail -

Oday Ali Hassen mail

link https://doi.org/10.54216/FPA.190111

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Using Lotka-Volterra Equations and Lightweight Post-Quantum Algorithm to Develop Lightweight Blockchain Security

Blockchain technology is now widely used in data sharing, cryptocurrency industry, Internet of Things and other fields. However, despite its increasing use, security and privacy concerns remain important issues. Blockchain security is enhanced by the use of hashing algorithms that ensure data integrity and provide a solution to security problems, but hashing algorithms usually have limitations in terms of resource consumption, memory and speed. To overcome these obstacles, the efficiency and security of the hashing algorithm used in blockchain must be increased. This paper presents a proposal to improve the hashing process in blockchain by leveraging the lightweight quantum algorithm Ascon, which has been improved after integrating it with nonlinear Lotka-Volterra equations. This integration can improve performance and security by combining the mathematical principles of these nonlinear equations to study the interactions between systems. Through this integration, it is possible to improve power management and work on intelligent resource allocation, as well as make the system more robust against attacks by complicating the random number generation process. The performance of the proposed system was tested in terms of throughput, elapsed time, amount of memory used, and time required to process data. The results showed that the proposed algorithm outperforms the original Ascon algorithm in terms of providing faster processing while maintaining a high level of performance and security, reducing time, and increasing the amount of data processed with less memory required for storage. These improvements are of great importance in developing blockchain technology and enabling its multiple uses in many applications. 

groups

Rasha Hani Salman mail -

Hala Bahjat Abdul Wahab mail

link https://doi.org/10.54216/FPA.190112

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

An IoT Framework for Emotion Detection and Behavior Influence: Towards Improving the Quality of Life

Accurate emotion detection is crucial for individuals facing communication barriers, yet existing approaches struggle with real-time limitations and information Individual privacy. This research presents a new IoT-based framework that integrates EEG and physiological signals from wearable sensors with deep learning models, including CNN, Decision Trees, SVM, KNN, and Naïve Bayes. Unlike traditional methods, our approach effectively mitigates data latency and sensor noise while ensuring compliance with GDPR and HIPAA standards. Experimental results demonstrate a validated accuracy of 99-100%, outperforming state-of-the-art models. These developments establish our framework as a game-changing instrument for affective computing applications, enhancing human-machine interaction and healthcare quality of life.

groups

Nada Asar mail -

Mohamed Handosa mail -

M. Z. Rashad mail

link https://doi.org/10.54216/FPA.190113

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

An examination of prolonged sitting ergonomic challenges in digital learning using TOPSIS and machine learning

The objective of the presented work is the examination of ergonomic challenges of prolonged sitting in digital learning using an instrumental multi-criteria decision-making technique named 'TOPSIS' (Technique for Order of Preference by Similarity to Ideal Solution). A total of sixteen ergonomic challenges of prolonged sitting in digital learning have been identified by a group dialogue with laptop, tablet, smartphone users, academicians, and students. The study compares equal weight ages and variable weight ages, finding that eye strain, neck pain, and mental tiredness are the most close to ideal solutions, while leg pain is the least. Linear Reggression, a machine learning approach, is the best-performing model, with Neural Network and SVM showing marginal improvement. The outcomes of the experiment demonstrate that the suggested model functions well in terms of accuracy, and techniques have been used to raise the diagnostic rate and solve the issue. The outcomes can be very helpful in finding and applying measures to deal with ergonomic challenges of prolonged sitting in digital learning. Policymakers may use the output of this study regarding the relative importance and productivity influencing tendency of these chosen sixteen ergonomic challenges, for creating mechanisms for the betterment of human-computer interface. 

groups

Manisha Sharma mail -

Hemant K. Upadhyay mail -

Udit Mamodiya mail -

Harish Reddy Gantla mail -

P. Satish mail

link https://doi.org/10.54216/FPA.190114

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Implement Intelligent YOLOv8 for Car Crowd Detection and Counting in the Roads

Car crowd management refers to the process of efficiently and safely managing the movement and flow of cars in crowded areas, such as parking lots, traffic intersections, event venues, and busy streets. Effective car crowd management is essential to ensure smooth traffic flow, prevent accidents, reduce congestion, and optimize the utilization of available parking spaces. It is a critical aspect of urban planning and traffic management to enhance the overall transportation experience and safety for both drivers and pedestrians. Deep learning methods are used to create an artificial system that is shown in this study. Proposed in detecting cars in streets and traffic intersections, in addition to determining the quantity of cars based on the YOLOv8 algorithm. Where the proposed system was trained on three types of datasets for the purpose of testing the algorithm used to determine the number of cars in each direction of the traffic intersection and then give priority to the most crowded direction with cars and then less and less. Where the system reached a high accuracy in detecting cars, reaching 98%, and through it conclude that the YOLOv8 algorithm used was suitable to be employed in solving the problem of determining the priority of traffic by identifying places of congestion with high accuracy.

groups

Noor Abdul Khaleq Zghair mail -

Rand A. Atta mail -

Hussein M. Hasan mail -

Asmaa S. Zamil mail -

Saja B. Attallah mail

link https://doi.org/10.54216/JISIoT.160111

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new