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International Journal of Neutrosophic Science | 2025

A Computationally Efficient Topologized Graphical Method for Neutrosophic Transportation Optimization: Cost Minimization, Performance Metrics, and Python Implementation

Pentagonal Neutrosophic Set is a powerful technique for modelling situation in real life where there is uncertainty, indeterminacy, and inconsistency, the PNTP is an advanced version of classical transportation problems. Traditional transportation models do not perform well with imprecise data unlike PNTP that offers a powerful framework that can handle truth, indeterminacy, falsity, non-membership, and membership parameters resulting in a more realistic decision about logistics. In this work, we present a novel Topologized Graphical Method (TGM) for resolving the PNTP, which uses graphical notations to visualize and analyse intricate interactions in transport networks under neutrosophic circumstances. In this paper, an efficient and structured solution methodology has been developed for optimization of PNTP, with TGM incorporated to provide a systematic approach to the PNTP while significantly reducing computational burden. To improve the pragmatism of the method, an algorithm is established in Python to convert the neutrosophic transportation model into a classical transportation problem, which contributes to computing efficiency and helps the decision-makers get the optimal solutions with little efforts. Solutions to numerical examples and case studies, which show that our method achieves better performance than conventional approach in minimizing transportation cost, optimizing resources allocation, and reducing the burden of calculation, provide validation of the proposed method. This research employs Pentagonal Neutrosophic Sets with the TGM as well as the use of the Python programming language to offer an effective and accurate decision-support instrument, improving transportation planning in uncertain dynamic environments. In addition, the findings provide tangible insights into how PNTP could be beneficial in real-world applications, particularly in fields like logistics, SCM, and network design, where managing uncertain information is essential. The next step of this work will be analysing the integration of AI and ML techniques with the presented method to gain improvements on predictive analytics, automation, and real-time decision-making abilities in transportation problems.

groups
Charles Robert Kenneth mail -
R. C. Thivyarathi mail -
E. Kungumaraj mail -
K. Sridharan mail -
V. Dhanasekaran mail -
K. A. Venkatesan mail
link https://doi.org/10.54216/IJNS.260310

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Leveraging Marine Predators Algorithm with Deep Learning Object Detection for Accurate and Efficient Detection of Pedestrians

Pedestrian detection using object detection and deep learning has been found to be effective method for identifying pedestrians in video frames or images accurately. It is more commonly used in many real-time applications, such as security observing systems, autonomous driving systems, and robotics. The combination of deep learning techniques and object detection algorithms allows efficient and robust detection of pedestrians in several real-time scenarios. However, it is necessary to improve the detection efficacy for complex environments such as cases with worse visibility due to weather or daytime, crowd scenes, and rare pose samples. Continuous improvement and research in DL algorithms, dataset collection, and TRA models contribute to accelerating the robustness and acc of pedestrian detection systems. Therefore, this research models a novel marine predator algorithm with DL-based pedestrian detection and classification (MPADLB-PDC) method. The objective of the MPADLB-PDC system lies in the accurate recognition and identification of pedestrians. To achieve this, the MPADLB-PDC technique involves two major processes, namely object detection and classification. In the first stage, the MPADLB-PDC technique uses an improved YOLOv7 object detector for the recognition of the objects in the frame. Next, in the second stage, the ensemble classifier comprises three classifiers such as deep feed-forward neural networks (DFFNNs), extreme learning machine (ELM), and long short-term memory (LSTM). To improve the recognition performance of the ensemble classifier, the MPA is used to optimally select the parameters related to it. The simulation outcome of the MPADLB-PDC technique was authorized on the pedestrian database, and the outcome can be studied in terms of various aspects. The experimentation values validated the better outcome of the MPADLB-PDC approach compared to other approaches.

groups
Hima Bindu Gogineni mail -
Hemanta Kumar Bhuyan mail -
E. Laxmi Lydia mail
link https://doi.org/10.54216/JISIoT.160103

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Feature Selection and Stability Analysis using Ensemble Techniques

Selecting the most relevant feature subset for a task is demanded and recommended for high accuracy and reduced model training time. Ensemble learning has shown superior results in classification; hence, we propose an ensemble method for feature selection and shown stability analysis for the selected feature set. The research question being investigated is whether ensemble methods are effective at selecting informative features in a dataset and if the selected features are stable compared to other feature selection methods. This paper presented a tree-based ensemble learning approach for feature selection. Our approach for ensemble feature selection includes function perturbation with the voting ensemble, an ensemble with a fixed number of features, and an ensemble with a contiguous number of features. Ensemble learning is found to be superior to other traditional feature selection algorithms. Ensemble learning algorithms are implemented on two high-dimensional microarray biomedical datasets. From our experimental study, it is observed that the voting ensemble outperforms other ensemble techniques, thereby reducing feature subset size and achieving higher accuracy. Stability analysis of all the algorithms has been studied and it is found that all ensemble techniques have higher stability than the traditional feature selection methods. Thus, ensemble learning proves to be a superior technique for feature selection. Our results demonstrate that the proposed method is effective in identifying relevant features and stable features and can improve the performance of machine learning models.

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Dipti Theng mail -
K. K. Bhoyar mail -
Prashant Pawade mail
link https://doi.org/10.54216/JISIoT.160104

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things |

Integrating IoT and smart AI for Enhanced Sustainability in freight forwarding companies Performance

The following study investigates the role and impact of IoT and Al technologies on operational efficiency, sustainability, and cost optimization of freight forwarding companies. Their goals are to measure the effects of these technologies on logistics performance, assess sustainability improvements like decreased carbon emissions and waste, and identify cost-saving drivers for AI and IoT integration. H1: The operational efficiency of IoT and AI should enhance information sharing, route planning, and warehouse management significantly H2 claims that it will contribute to the reduction of carbon emissions and waste production by allowing real-time tracking, optimizing the usage of materials throughout the production cycle. H3- Cost Reduction in Logistics Operations through AI-based Automation, Predictive analytics and Improved Asset Management The approach was a quantitative research design, and data were obtained from 240 respondents from five large freight forwarders (companies): DHL Global Forwarding; Kuehne + Nagel; DB Schenker; XPO Logistics; and CEVA Logistics. Objective: Improvements after adoption are analyzed using structured questionnaires to measure key performance indicators (KPI) and frequency analysis and percentage calculation methods. The results confirm the transformative role of IoT and AI in freight logistics, increasing operational efficiency, sustainability, and cost efficiency. Logistics performance must be further optimized through continued investment in digital innovation.

groups
Apeksha Garg mail -
Sudha Vemaraju mail
link https://doi.org/10.54216/JISIoT.160105

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

An Enhancement of YOLOV3-Tiny Model for Turmeric Plant Disease Detection

Turmeric is a rhizomatous crop recognized for its medicinal effects which requires significant observation to ensure appropriate growth and progression. Turmeric plant diseases cause yield losses impacting food production systems and causing economic losses. Early prevention of these diseases is crucial for improving agricultural productivity. For this reason, The Improved YOLOV3-Tiny Model (IY3TM) was developed using Cycle-GAN and Convolutional Neural Network (CNN) with residual network for the early turmeric plant disease detection. However, this model leads to the omission of vital details along with the exact positioning of key attributes, thereby decreasing prediction accuracy. To resolve this, Convolutional and Vision Transformer model for Turmeric Diseases Detection (ConViT-TDD) is proposed for the prediction of turmeric plant diseases. ConViT-TDD is integrated into IY3TM with a self-attention mechanism and CNN-based global perspective to enhance the performance of the model A ConViT-TDD block involves the input channel transformation, the channel as well as spatial attention mechanism and global-minded transformers. The input channel transformation utilizes a convolutional layer to minimize the dimension of input channel and reduces the computational complexity. Global-minded transformers generate a feature vector based on the input channel transformation that is then transmitted to the encoder component. By collecting channel weights and spatial weights, respectively, the channel and spatial attention modules enhance the model's sensitivity to certain channel attributes and spatial locations, hence altering the feature representation of those channels and spatial locations. The attention module can adaptively change the weights of channel and spatial features for improved feature extraction and fusion. Once the initial attributes are reformed, the IY3TM detects and classifies the turmeric plant diseases. The test outcomes reveal that the ConViT-TDD model accomplishes an overall accuracy of 93.16% on the collected turmeric plant diseases images which is contrasted with the classical CNN models.

groups
Shylaja Santhosh mail -
Revathi Thiyagarajan mail
link https://doi.org/10.54216/JISIoT.160106

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Leveraging Variational Autoencoder with Hippopotamus Optimizer-Based Dimensionality Reduction Model for Attention Deficit Hyperactivity Disorder Diagnosis Data

Adverse Drug Reactions (ADRs) are very hazardous to patients. Thus, the detection of ADR intends to automatically distinguish, which is an intensive study for public health monitoring functions.  Detecting ADRs is the most significant information to determine the patient’s opinion on some drugs. As patients can experience projected and occasionally unpredicted negative results from taking some drugs, late detection of ADRs may place life-threatening dangers to patients; posing significant financial, social, and legal consequences to the regulatory agencies and manufacturing companies. The usage of medical data, like states and electronic health records (EHR), became normal in offering a richer understanding of health services and assisting ADR analysis. Developments in deep learning (DL) and machine learning (ML) have made several analytic models have the potential to apply higher-dimensional data to predict adverse effects. In this study, we present a Hippopotamus Optimizer-Based Feature Selection for Adverse Drug Reaction Detection Using a Variational Autoencoder (HOFS-ADRDVAE) model. The main intention of the HOFS-ADRDVAE model is to provide an automatic system for the detection of ADR using state-of-the-art techniques. Initially, the data normalization stage employs min-max normalization for converting input data into a beneficial format. In addition, the feature selection process has been executed by the hippopotamus optimization (HO) algorithm. Besides, the proposed HOFS-ADRDVAE model designs a variational autoencoder (VAE) technique for the classification procedure. At last, the Hunger Games search (HGS) algorithm-based hyperparameter selection process is executed to optimize the classification results of the VAE system. A wide-ranging experiment was implemented to point out the performance of the HOFS-ADRDVAE method. The experimental outcomes specified that the HOFS-ADRDVAE model emphasized improvement over another existing method.

groups
N. Deepaletchumi mail -
R. Mala mail
link https://doi.org/10.54216/JISIoT.160107

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Enhanced Feature Selection Approach using Artificial Hummingbirds with Genetic Algorithm

Feature selection (FS) is a crucial preprocessing step in data mining to eliminate redundant or irrelevant features from high-dimensional data. Many optimization algorithms for FS often lack balance in their search processes. This paper proposes a hybrid algorithm, the Artificial Hummingbird Algorithm based on the Genetic Algorithm (AHA-GA), to address this imbalance and solve the FS problem. The main goal of AHA-GA is to select the most crucial characteristics to improve overall model categorization. The UCI datasets are used to assess the performance of the proposed FS method. The proposed feature selection algorithm was compared with five feature selection optimization algorithms: BWOAHHO, HSGW, WOA-CM, BDA-SA, and ASGW. AHA-GA achieved a classification accuracy of 96% across 18 datasets, which was higher than BWOAHHO (93.2%), HSGW (92.5%), WOA-CM (94.4%), BDA-SA (93%), and ASGW (91.6%). When comparing the proposed AHA-GA algorithm to the results obtained by the other five algorithms in terms of selected attribute size, the average feature sizes were as follows: AHA-GA (15.10889), BWOAHHO (16.74222), HSGW (19.43111), WOA-CM (17.05389), BDA-SA (17.275), and ASGW (19.7585). The statistical and experimental tests demonstrated that the proposed AHA-GA performs better than competitive algorithms in selecting effective features.

groups
Ismael Salih Aref mail -
Dheyab Salman Ibrahim mail -
Bashar Talib Al-Nuaimi mail
link https://doi.org/10.54216/JISIoT.160108

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Multi-Dimensional Trust based Data Dissemination mechanism (MDTD) for Ensuring Authentication by Eliminating Blackhole Attack in VANET

Vehicular Ad Hoc Networks also known as VANET, and it is a special type of ad hoc networks since it is deployed on demand.  Here the nodes are representing as vehicles, and they are communicating with each other to ensure the reliable and secure safety driving. Since it is open environment, ensuring secure routing is always a challenging task. Routing is one of the essential things in ad hoc networks because it is carrying road safety information always. However, most of the time, it is affected by attacks. Black hole is one of the attacks where the malicious nodes that is black hole vehicles advertise itself that having the shortest path to the destination by the way it tries to disturb the entire environment. In this paper, multi-dimensional trust-based data dissemination mechanism is proposed. The main objective is to ensure authentication by eliminating black hole attack. The proposed method makes use of multiple trusts such as direct, indirect, integrity, intimacy, and mobility over Dynamic Source Routing (DSR) protocol by the way authentication can be achieved. Simulation results shows that the proposed model works efficiently compare with existing models.

groups
C. Balakumar mail -
S. Vydehi mail
link https://doi.org/10.54216/JISIoT.160109

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Robust Zero-Day Attack Detection with Optimal Deep Learning for Securing Internet of Things Environment

The Internet of Things (IoT) aims to provide connectivity between all computing entities. However, this facilitates cyberthreats, which exploits the existence of vulnerability over a period. The zero-day threat is one of the vulnerabilities that can result in zero-day attacks that are destructive to the network security and an enterprise. This attack may have potentially compromised critical infrastructure, far-reaching consequences, national security, and even personal privacy. To alleviate the risks, organizations and manufacturers should prioritize proactive security measures, involving robust authentication mechanisms, ongoing monitoring, and timely software updates, to defend the IoT ecosystem from emerging threats. In present scenario, deep learning (DL)-based models have improved robustness in learning data giving it an improved capability to identify unknown information, since it can able to extract knowledge of non-linear data to identify unknown information. The study presents a Robust Zero-Day Attack Detection with Optimal Deep Learning (RZDAD-ODL) technique for the IoT framework. The primary intention of the RZDAD-ODL model lies in the automatic and effectual detection of zero-day attacks in the IoT framework. In the presented RZDAD-ODL technique, the honey badger algorithm (HBA) can be used for the optimum range of the features. Besides, the RZDAD-ODL technique exploits the conditional variational autoencoder (CVAE) model for attack detection and its parameter tuning process can be performed by using a rider optimization algorithm (ROA). The experimentation results of the RZDAD-ODL system can be validated on a benchmark dataset. Extensive comparison studies reported the better attack detection performance of the RZDAD-ODL model over other current techniques.

groups
Nahla J. Abid mail -
Nawaf Alhebaishi mail -
Turki Althaqafi mail
link https://doi.org/10.54216/JISIoT.160110

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new
Journal of Cybersecurity and Information Management | 2025

AI-Driven Features for Intrusion Detection and Prevention Using Random Forest

In this research, we investigate sophisticated methods for Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS), leveraging AI-based feature optimization and diverse machine learning strategies to bolster network intrusion detection and prevention. The study primarily utilizes the NSL-KDD dataset, an enhanced version of the KDD Cup 1999 dataset, chosen for its realistic portrayal of various attack types and for addressing the shortcomings of the original dataset. The methodology includes AI-based feature optimization using Particle Swarm Optimization and Genetic Algorithm, focusing on maximizing information gain and entropy. This is integrated with the use of Random Forest (RF) to reduce class overlapping, further enhanced by boosting techniques. Grey Wolves Optimization (GWO) alongside Random Forest. This innovative approach, inspired by grey wolf hunting strategies, is employed for classification tasks on the NSL-KDD dataset. The performance metrics for each intrusion class are meticulously evaluated, revealing that the GWO-RF combination achieves an accuracy of 0.94, precision of 0.95, recall of 0.93, and an F1 score of 0.94.

groups
Mohammed B. Al-Doori mail -
Khattab M. Ali Alheeti mail
link https://doi.org/10.54216/JCIM.160101

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new