Secure Real-Time Information Sharing in Artificial Intelligence Driven Freight Forwarding for Green Supply Chains

Apeksha; Sudha

doi:https://doi.org/10.54216/JCIM.160209

Fusion: Practice and Applications | 2025

Systematic Review of VLC-Based NOMA Using Machine Learning Algorithms

Visible light communication (VLC) integrated with nonorthogonal multiple access (NOMA) is a promising technique to meet the increasing demand for high capacity, energy-efficient communication in forthcoming 6G networks. This work thoroughly evaluates VLC-NOMA systems and emphasizes the incorporation of machine learning (ML) approaches to improve spectrum efficiency, the bit error rate, and resource allocation. A technique based on Preferred Reporting Items for Systematic Reviews and Meta-analyses produced 244 records, among which 45 were selected for comprehensive study. The review identified obstacles, including scalability, computational complexity, and insufficient experimental validation. A comparative examination elucidated the strengths and limits of machine learning methodologies, including machine learning, deep neural networks, and federated learning, in addressing these difficulties. The study identified key research gaps, proposed future directions, and emphasized the need for hybrid optimization techniques, lightweight machine learning models, and real-world implementations. The findings contribute to the development of robust, scalable VLC-NOMA systems for 6G applications.

groups

Ayah A. Hameed mail -

Lwaa F. Abdulameer mail -

Heba M. Fadhil mail

link https://doi.org/10.54216/FPA.200104

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

The Adoption of Artificial Intelligence for Higher Education Sustainability

Business executives and scholars maintain that Artificial Intelligence (AI) is positioned alongside pivotal human inventions and advancements such as fire, electricity, and the incandescent light bulb. By harnessing AI technologies, academic institutions can augment pedagogical approaches, elevate the caliber of education, and furnish learners with novel avenues to cultivate their proficiencies and competencies. However, on the contrary, the implementation of AI in higher education has provoked deliberations regarding whether institutions ought to prohibit its utilization entirely or promote its integration to enhance educational outcomes. Nevertheless, despite the escalating acknowledgment of AI's importance in the educational sphere, there needs to be more thorough exploration concerning its adoption and comprehending its impacts. Data was collected from 300 respondents to fill this gap by building on the 'Unified Theory of Acceptance and Use of Technology' (UTAUT) model. We empirically contribute to the existing literature by clarifying the fundamental factors that affect the adoption of AI within higher education, in addition to scrutinizing the consequences of AI on knowledge acquisition. Moreover, we elucidate the moderating effects of workload and temporal limitations. The findings provide substantial insights relevant to the incorporation of AI for knowledge acquisition in higher education and are anticipated to provoke further scholarly discussion.

groups

Soliman Aljarboa mail -

Abdulatif Alabdulatif mail -

Makhmoor Bashir mail

link https://doi.org/10.54216/FPA.200105

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Novel IoT based Wavelet and PCA Approach for Improved Glaucoma Classification Using Retinal Images

The proposed research implements a new 3D-block-based alpha-rooting enhancement method, which uses PCA classification for detecting glaucoma. The use of Euclidean distance in current image enhancement methods tends to lose important structural details that result in incorrect classification outcomes. The proposed method executes block-matching and grouping operations to locate equivalent 3D patterns before using adaptive alpha-rooting adjustment, which automatically controls contrast throughout optic disc and optic cup regions. Following enhancement processing an additional polishing stage optimizes these results for classification purposes. The classification of enhanced images takes place using PCA and its wavelet variants to extract important retinal features. The proposed system utilizes both ACRIMA dataset and real-world hospital images to show better classification achievements than CLAHE-based enhancement while validating its effectiveness. The experimental outcome demonstrated both high accuracy and reduced time consumption when using biorthogonal DWT with (2D) ²-PCA for classification. The proposed method offers a time-effective hardware-oriented solution for automatic glaucoma detection by combining conventional statistical techniques with deep learning-based classification approaches. The method provides clinical facilities with a dependable standard for glaucoma identification and diagnosis improvement. The Proposed 3D block-based adaptive alpha rooting method achieves a total accuracy level of 95.1%. The U-net model achieves 91.0% accuracy while CNN reaches 90.3% and RF delivers 87.1%. At the same time, SVM provides 86.3% accuracy while PCA returns 85.2% and DWT reaches 84.2% and KNN establishes 81.2% accuracy.

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Vivek Jain mail -

H. Shree Kumar mail -

Hemant Sharma mail -

R. Kiran Kumar mail -

Chandrasekaran Raja mail -

Krishna Kishore Thota mail

link https://doi.org/10.54216/JISIoT.170113

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Hybrid Compressive Sensing based Secure Medical Image Compression and Reconstruction in Telemedicine Application

The transmission of complex medical images in telemedicine applications poses significant challenges. An effective hybrid compressed sensing and encryption framework is proposed for enabling efficient MRI compression and secure transmission in telemedicine applications. Firstly, a fuzzy-logic-based image enhancement is pressed. Then an optimized chaotic sequence generation scheme is formulated based on image characteristics to achieve compression robustness and security of the compression process. In addition, the proposed framework uses a lightweight public key encryption method to speed up encryption and decryption time. Our experimental results demonstrate the effectiveness of the proposed system on various metrics, including PSNR, SSIM, correlation coefficient, and processing time. The system consistently achieved high SSIM scores (0.96 to 1.0) and maintained low algorithm processing time, validating its efficiency in high-quality reconstruction.

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Shilpa A. N. mail -

Santosh Kumar G. mail -

Veena C. S. mail

link https://doi.org/10.54216/JISIoT.170114

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

An Optimized Routing Algorithm for Internet of Vehicle (IoV) Environment

Internet of Vehicles (IoV) is the later application of VANET and is the fusion of the Internet and IoT. With the advancement in innovation, individuals are investigating a traﬃc environment wherever they would have the extreme cooperation with their environment including other vehicles. The Internet of Vehicles (IoV) was created so that vehicles can communicate with each other in an infrastructure environment. The prerequisite is to form a more secure trip in an IoV environment with the least delay and high packet delivery rate. This guarantees that all information is received with negligible delay to maintain a strategic distance from any mishap. This paper presents a new position-based routing algorithm called Position-Based Connectivity Aware Routing (PBCAR) for IoV that covers sparse and coarse regions of vehicles. It takes advantage of the Internet and street format to progress the execution of routing in IoV. The PBCAR algorithm uses a GPS real-time chasing system to find traffic information for forming position-based paths from the source node to the destination node. The PBCAR algorithm has been simulated using SUMO and Network Simulator and compared with AODV and GPSR. The results show that the PBCAR algorithm obtains exceptional results considering the several simulation parameters.

groups

Ravi Shankar Shukla mail

link https://doi.org/10.54216/JISIoT.170115

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Image recognition via Local 3-bit Binary Patterns

The current study introduces a trainable object detection model that can be taught to detect an object of a given class within an unconstrained scene. The researchers of the current study use this advanced system in the detection of Relics images, which involves a calculation of Local 3bit Binary Patterns (3bit-LBP). The key highlights of the current work include the integration and analyses of the utilization of the Multi-Support Vector Machine Classification (MSVMC) and Integral image computation analysis. The experimental outcomes of the current study indicate that the method of 3bit-LBP is superior to other methods in accuracy and stability, especially when images of different illumination and object rotation were tested. The researchers further conducted a comparative performance evaluation showing that the presented system gives better detection rates as compared to the conventional strategies, revealing the efficiency in real-world applications. Finally, it is important to note that the implications of the results can be applied to uses beyond just relic detection. To conclude, the current work advances the knowledge of how to improve the functionality of object recognition algorithms further in the context of image recognition systems.

groups

Abdulaziz Saleh Alraddadi mail -

Essam O. Abdel-Rahman mail

link https://doi.org/10.54216/FPA.200106

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Hybridization of Deep Learning Model with Optimization Algorithm for DNA Based Genetic Disorders Detection and Classification

Genetic diseases are diseases produced by anomalies in the DNA of the person. These abnormalities may be larger-scale chromosomal mutations or irregularities in the particular gene. These diseases significantly influence some body functions and systems and are hereditary or develop automatically. Traditional models such as genetic testing and karyotyping might fail to identify complex or rare modifications, requesting more detailed techniques namely whole-genome sequencing (WGS). In recent decades, regardless of important technological evolution, uncommon genetic diseases continue to cause problems, with a significant portion of patients (50–66%) remaining unidentified according to clinical condition alone. An accurate analysis is important to provide equal support to patients and their relations, despite particular therapeutic intrusions. Presently, machine learning (ML), and in detail the DL subspecialties, have been utilized to determine clinically relevant prediction devices in other medical areas. For mental disorders, ML methods have presented major promise in forecasting either diagnosis or prediction in mental disorders. In this manuscript, we design and develop a Hybrid Deep Learning and Metaheuristic Optimization Algorithm for Detecting Genetic Disorders (HDLMOA-DGD) model. The proposed HDLMOA-DGD algorithm's main goal is to detect and classify genetic disorders using an advanced deep-learning model. At first, the Z-score normalization is employed in the data pre-processing phase for converting an input data into a uniform format. Moreover, the proposed HDLMOA-DGD model implements a hybrid deep learning model of the temporal convolutional network, bi-directional long- and short-term memory network, and Self-Attention mechanism (TCN-BiLSTM-SA) technique for the classification process.  At last, the modified gannet optimization algorithm (MGOA)-based hyperparameter selection process is performed to optimize the detection and classification results of the TCN-BiLSTM-SA system. The experimental validation of the HDLMOA-DGD model is verified on a benchmark dataset and the results are determined regarding several measures. The experimental outcome underlined the development of the HDLMOA-DGD model in the genetic disorder detection process.

groups

S. Puvaneswari mail -

G. Indirani mail

link https://doi.org/10.54216/FPA.200107

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Some Results on Neutrosophic Graphs in Neutrosophic Topological space

A tools and techniques of neutrosophic graph have found many applications in different areas such as topology, networks, computer of science, etc. In addition, neutrosophic graph is a generalization of intuitionistic fuzzy graph. Therefore, in this paper we study some characteristics of neutrosopheic graphs (NTCG) and some basic definitions. Moreover we investigate several kinds of arcs,  -strong, -strong,  -arc, and  -strong, -strong,  –arc in neutrosopheic graphs (NTCG) , Finally we give neutrosophic -bridge and neutrosophic -bridge (NTC -bridge) and some interesting properties of  neutrosophic bridge (NTCB), which is being taught for the first time, and obtain several important properties.

groups

Gazwan Haider Abdulhusein mail -

Dalia Raad Abd mail -

Wadei Faris AL-Omeri mail

link https://doi.org/10.54216/IJNS.260319

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Explainable Artificial Intelligence Driven Intrusion Detection System for Enhancing Reliability and Interpretability in IoT Based Network Security Solutions

The implementation of Intrusion Detection Systems (IDS) remains crucial for network security yet high-dimensional data alongside class imbalance issues decrease their functionality. Machine learning-based IDS models, which use traditional approaches experience difficulties in providing explanations about their prediction results. An IDS framework enhancement with explainable AI (XAI) methods aims at improving the system's transparency throughout this study. The data processing includes KNN imputation combined with K-Means SMOTE to handle missing information and class imbalance problems. When selecting features the model uses a merged methodology combining Pearson Correlation with Mutual Information and Sequential Forward Floating Selection (SFFS) algorithms for optimization. Light Gradient Boosting Model (LGBM) serves as the classification model that produces higher accuracy than competing methods with 90.71% for UNSW-NB 15 and 96.98% for CICIDS-2017. By using SHAP-based explain ability, the system provides worldwide and specific model interpretations that enable users to trust IDS prediction results. The experimental findings validate that the proposed methodology succeeds in simplifying the system while improving its classification functionality and delivering stronger interpretability properties to tackle weaknesses of current IDS technologies. The examination presents important findings for the development of secure network protection technologies, which operate with transparency.

groups

Purshottam J. Assudani mail -

N. V. S. Pavan Kumar mail -

K. Mohanambal mail -

R. Chitra mail

link https://doi.org/10.54216/JISIoT.170116

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2025

Secure Real-Time Information Sharing in Artificial Intelligence Driven Freight Forwarding for Green Supply Chains

The integration of artificial intelligence (AI) and real-time information sharing is transforming the freight forwarding industry, enabling more sustainable and efficient green supply chains. However, the increasing reliance on interconnected systems raises significant cybersecurity challenges, particularly regarding secure data exchange and protection of sensitive information. This paper explores the critical role of cryptographic models and secure communication protocols in safeguarding real-time data sharing among AI-driven logistics networks. We analyze key security challenges faced by IoT-enabled freight systems and propose robust encryption and key distribution strategies to ensure confidentiality, integrity, and resilience. Our findings highlight the importance of secure information management in advancing sustainable, cyber-resilient supply chains that support environmental goals while maintaining operational efficiency.

groups

Apeksha Garg mail -

Sudha Vemaraju mail

link https://doi.org/10.54216/JCIM.160209

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new