Fixed point results in ωt-distance mappings for Geraghty type contractions

Ammar Al-tawil; Ayman. A Hazaymeh; Anwar Bataihah

doi:https://doi.org/10.54216/IJNS.260101

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

International Journal of BIM and Engineering Science | 2025

Concrete Waste Management Based on BIM for Syrian Buildings

This study focuses on the management of concrete waste from demolished buildings. It is a crucial issue globally and particularly in Syria due to the significant amounts of concrete waste resulting from the long war and the February 6, 2023 earthquake. The research aims to promote sustainability and resource conservation in the Syrian construction sector by introducing a method for managing demolition waste using Building Information Modeling (BIM) technology. A case study was conducted on a residential building in the old city of Homs that was demolished due to the war. The building was modeled using the Revit software, and mathematical modeling was applied to calculate and manage the demolition waste related to the building's structural frame. The study revealed potential economic savings of up to 4.2% of the total cost of the building's concrete framework through recycling the structural frame waste (coarse aggregate + fine aggregate only). Furthermore, the study estimated the financial returns that could be realized from managing demolished concrete waste across the entire Homs Governorate.

groups

Mohammed Hasan mail -

Lama Saoud mail

link https://doi.org/10.54216/IJBES.100206

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Enhancing Security in Cloned Nodes: An Intelligent Framework for Attack Detection and Mitigation using Deep Learning with Optimization Algorithm in Wireless Sensor Networks

Wireless Sensor Network (WSN) signifies a state-of-the-art technology that combines energy-effective sensors with wireless transmission services enabling prompt surveillance and data collecting from the nearby environments. Owing to the intrinsic features of WSNs, they face numerous challenges of security that range from resource-based attacks, like computational overload or energy depletion, to interception, eavesdropping, and tampering. With the hacked data, the attackers can replicate the same sensors and use clones in the corresponding WSNs. This kind of cloning of the sensors, which is comprised of the WSN, is called a clone attack. Since the replicated sensors formed by the attackers have parallel keys and information, therefore the clone attacks have become a great attack for WSN. To defend WSNs against cyberattacks, machine learning (ML) and deep learning (DL) were applied to classify malicious and normal traffic. This study designs an Attack Detection and Mitigation using Deep Learning with an Optimization Algorithm in Wireless Sensor Networks (ADMDL-OAWSN). The main objective of the ADMDL-OAWSN system is to improve security in cloned nodes for the cyberattack detection model. In the primary step, the data pre-processing employs the StandardScalar method to transform input data into a suitable format. Next, the proposed ADMDL-OAWSN model designs a crayfish optimization algorithm (COA) for the subset of the feature selection (FS) to pick the most related features from an input dataset. For the attack classification process, the convolutional neural network and bi-directional gated recurrent unit with attention mechanism (CNN-BiGRU-A) technique have been exploited. At last, the parameter tuning of the CNN-BiGRU-A is applied by the design of the secretary wolf bird optimization (SeWBO) algorithm. Extensive experiments have been conducted to validate the results of the ADMDL-OAWSN system. The simulation results revealed that the ADMDL-OAWSN system emphasized furtherance when compared to other recent systems

groups

P. Kalvikkarasi mail -

K. Selvakumar mail

link https://doi.org/10.54216/FPA.190115

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Robustness of Ensemble Deep Learning Model with Zebra Optimization Algorithm for Weather-Related Disaster Detection System Using Remote Sensing Images

Weather monitoring is a vital challenge in dissimilar areas of applications such as military missions, higher precision agriculture, outdoor entertainment and recreation, industrial manufacture, and logistics. The most vital application is natural weather disaster monitoring. Weather change has made stronger an occurrence of natural disasters all over the world. More extreme climate events have been experienced for the past few years, like lower and higher temperatures, sturdy winds in humid cyclones, heavy rains, and intensified lack. Therefore, at present, remote sensing imagery (RSI) analysis is necessary in the field of ecological and weather monitoring mainly for the application of identifying and handling a natural climate disaster. To upsurge the accuracy of detection, machine learning (ML) and deep learning (DL) systems were applied to enhance the efficacy of removing features and help to perceive large-scale losses like landslides, earthquakes, and floods. In this manuscript, we design and develop a Weather Disaster Detection Model Using Zebra Optimization Algorithm with Ensemble Learning on Remote Sensing Images (WDDZOA-ELRSI) technique. The proposed WDDZOA-ELRSI model's main intention is to improve the detection model of weather disasters using state-of-the-art DL methods. Initially, the bilateral filter (BF) method is employed in the image pre-processing stage to eliminate the unwanted noise from input data. Furthermore, the feature extraction method executes GoogleNet technique to transform raw data into a reduced set of relevant features. For the classification process, the ensemble of deep learning models such as conditional variational autoencoder (CVAE), graph convolutional network (GCN), and Elman recurrent neural network (ERNN) have been deployed. Eventually, the zebra optimization algorithm (ZOA)-based hyperparameter tuning procedure has been achieved to improve the detection outcomes of ensemble models. The simulation analysis of the WDDZOA-ELRSI system is verified on a benchmark image dataset and the outcomes were evaluated under numerous measures. The simulation outcome emphasized the enhancement of the WDDZOA-ELRSI model in the weather disaster detection process

groups

Daniel Arockiam mail -

Azween Abdullah mail -

Valliappan Raju mail

link https://doi.org/10.54216/FPA.190117

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025