Multi-objective Optimization in Satellite-Assisted UAVs

Mohammed Ahmed; Shaima Miqdad Mohamed; Kifaa Hadi; Mujahid Hamood Hilal; Fatima Hashim; Rabei Raad

doi:https://doi.org/10.54216/JISIoT.160116

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

Journal of Intelligent Systems and Internet of Things | 2025

Efficient CH selection for Traffic Congestion Reduction and To Improve Network Connectivity in Vehicular Adhoc Networks

Vehicular ad hoc network (VANET) is an innovative technology that has attracted many researchers and the industrial sector. The increase in vehicle movement and the requirement for effective traffic management systems have resulted in the development of VANETs. The Super Cluster Head based Efficient Traffic Control (SCHETF) model aims to alleviate traffic congestion and decrease energy consumption in VANETs through a novel integration of Cluster Head (CH) election, cluster gateway formation, and effective data transmission. SCHETF utilizes a parameter-driven CH election process that considers factors such as network connectivity, distance, speed, and trust levels. This approach guarantees the most suitable CH selection, reducing energy expenditure while enhancing network efficiency. The model assesses network connectivity through indicators like traffic flow and lane weights, ensuring precise determination of link reliability. Metrics for distance and speed are normalized to evaluate the changing behavior of vehicles, while trust ratings are given based on historical and community information to improve reliability. The creation of cluster gateways reduces unnecessary cluster formations by implementing Cluster Gateway Creation (CGC) at strategic sites, lessening communication load, and boosting cluster stability. Efficient data transmission is accomplished by appointing several Cluster Gateway (CGW) within clusters. A backoff timer mechanism gives priority to the CGW that is farthest from the CH for message forwarding, avoiding unnecessary repetitions and guaranteeing effective message dispatch. The model is smart clustering and gateway strategies lessen signaling load during handovers and enhance resource management in dynamic vehicular settings. The SCHETF model offers a thorough framework for tackling the challenges faced by VANETs, providing scalable and energy-efficient communication options. This improves data distribution, assures dependable connectivity, and plays a crucial role in the progress of intelligent transportation systems. The model has been put into practice through experimentation in Network Simulator 2 (NS2). The parameters considered in this study encompass energy efficiency, throughput, packet delivery ratio, end-to-end delay, packet loss, and routing overhead. To undertake a comparison study, the developed SCHETF findings are compared to older approaches such as Evolutionary Algorithm-based Vehicular Clustering Technique (EAVCT), Region Collaborative Management for Dynamic Clustering (RCMDC), and Novel Hypergraph Clustering Model (NHGCM). The outcomes indicate that the suggested SCHETF strategy outperforms previous methods.

groups

Mohammed I. Khalaf mail -

Ahmed Jamal Ahmed mail -

Hazim Noman Abed mail -

Mahmood AlSaadi mail

link https://doi.org/10.54216/JISIoT.160112

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Human to Chatbot Text Classification Using Multi-Source AI Chatbots and Machine Learning Models

The fast growth of artificial intelligence technologies, especially language processing technology has obscured the lines in between human-generated text comparing to chatbot-generated message.  Recognizing which generated such, a text is essential for applications like information generating and manipulated text in order to guarantee authenticity between communicated parties. This research applies to a set of machine learning models to identify text as either human-written or chatbot-generated. The methodology of this research starts with a dataset including text generated from different Large Language Models (LLMs) along with a text generated by a human.  After that, Tf-Idf ranking vectorization was used to define word embedding has and represent the text numerically. Then, different Machine Learning (ML) models leveraged recognize whether a human or a chatbot generated a text. The ML models applied include Logistic Regression, Random Forest, Decision Tree, Gradient Boosting, Naïve Bayes, and XGBoost.  For this study accuracy, precision, recall, F1-score were used to evaluate the system. The dataset first was split into 80% for training and 20% for testing. Out of all implemented models, the Random Forest model reported the best with accuracy of 88%. Logistic Regression reported a close accuracy of 85%. The Random Forest model showed an 8% improvement compared to previous studies that reported an accuracy of 80%. Confusion matrices revealed that the Random Forest model provided high precision and recall, minimizing classification misleading of human or chatbot text. The research focused on studying the ability of ML models in identifying human vs. chatbot-generated text. The results showed the RF model was the best among other models with 88% accuracy. This accuracy shows a possible usage of such models in real-world applications that requires the confidentiality of human writing.

groups

Mohammed Salah Ibrahim mail -

Jabbar Abed Eleiwy mail -

Hassan Mohamed Muhi-Aldeen mail -

Yusra Al-Yasiri mail -

Ahmed Adil Nafea mail

link https://doi.org/10.54216/JISIoT.160113

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Improving Non-Dominated Sorting Genetic Algorithm for IOT Service Composition Considering National Energy Consumption and User Experience

This paper proposes an enhanced Non-Dominated Sorting Genetic Algorithm -II algorithm to optimize IoT service composition by incorporating national energy consumption requirements and user experience, areas often overlooked in traditional models that primarily focus on time, cost, and quality. The original NSGA-II algorithm is prone to premature convergence and local optima issues during population iteration. To address these limitations, we introduce a novel evaluation model and improve the elite retention strategy of the NSGA-II algorithm. The improved algorithm balances exploration and exploitation through dynamic crowding distance adjustment and adaptive selection pressure, enhancing diversity and avoiding local optima. Experimental results demonstrate that the I-NSGA algorithm not only reduces running time by 5.916% but also achieves a smoother Pareto surface, indicating a more optimal distribution of solutions. The novelty of this approach lies in its comprehensive inclusion of energy consumption and user experience, the timeliness in addressing emerging IoT optimization challenges, and the relevance to current IoT service composition needs.  This validates the effectiveness and advancement of the proposed model and algorithm, providing a robust and efficient solution for IoT service composition optimization.

groups

M. Bheemalingaiah mail -

G. Sreenivasulu mail -

L. Venkateswa Reddy mail -

Khaja Mahabubullah mail -

A. Ramesh Babu mail -

D. Himagiri mail

link https://doi.org/10.54216/JISIoT.160114

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A New Descriptor Based on Machine Learning for Intrusion Detection in Wireless Sensor Networks WNSs

Wireless sensor networks have become a vital component of the infrastructure for many modern applications. With the increasing use of wireless sensor networks, the challenges facing these networks in the field of security are escalating and growing, and with the rapid advancement of wireless communication technology, these networks are exposed to increasing, complex and continuous threats. Our research is characterized by innovation in the field of security technology to enhance protection, repel attacks and detect intrusions, among these innovations are intrusion detection systems based on machine learning as a creative and new solution. In this research, we highlight the effectiveness of different machine learning algorithms, such as supervised and unsupervised learning, in detecting anomalies and intrusions within wireless sensor networks, as our goal focuses on enhancing the security of wireless sensor networks (WSNs) by adopting intrusion detection systems (IDS) based on machine learning techniques. In this context, with a focus on using the WSN-DS dataset. The results of this research showed that machine-learning models could improve the security efficiency of wireless sensor networks by achieving accuracy ranging from 91% to 99.7% and testing time ranging from 0.006 to 0.1249, which enhances the ability to effectively retrieve and detect threats in real time.

groups

Esraa Saleh Alomari mail -

Oday Ali Hassen mail -

Wisam Makki Salim mail -

Selvakumar Manickam mail -

Nur Azman Abu mail

link https://doi.org/10.54216/JISIoT.160115

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Multi-objective Optimization in Satellite-Assisted UAVs

Nowadays, Vehicular communication is used in intelligent transmission applications. The number of vehicles used in a particular region has numerously increased energy consumption, computation delay, and computation overhead. In this paper, Multi-Objective Optimization in Satellite Assisted UAVs (MO-SAUVs) is proposed under an improved Ant Colony Optimization (IACO) algorithm. The procedures that are considered for the process of MO are optimal logistics distribution, path prediction-based pheromone deposition, and evaporation. Using this method, effective region selection for the UAVs is performed which leads to improving the network energy efficiency by decreasing energy consumption and delay. The simulation is performed in NS2 and the proposed MO-SAUAVs method is compared with the TA-SAUAVs method and PL-SAUAVs method according to different parameters. The results show that the proposed MO-SAUAVs method achieves lower computation delay (70ms to 110ms), higher energy efficiency (6% to 16%), lower energy consumption (7% to 14%), and packets lower computation overhead (500 packets to 700) when we were compared with TA-SAUAVs and PL-SAUAVs.

groups

Mohammed Ahmed Jubair mail -

Shaima Miqdad Mohamed Najeeb mail -

Kifaa Hadi Thanoon mail -

Mujahid Hamood Hilal Alzakwani mail -

Fatima Hashim Abbas mail -

Rabei Raad Ali mail

link https://doi.org/10.54216/JISIoT.160116

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new