Enhancing Task Scheduling Process in Fog Computing using GTO-SSSA: A Metaheuristic Approach

V.; R.; T.; T.

doi:https://doi.org/10.54216/JISIoT.140109

Galoitica: Journal of Mathematical Structures and Applications | 2024

On the Numerical Solutions Based on a Novel Hybrid Method for Some VNDEs Problems

This paper is devoted to find the solution of the Vanishing Neutral Differential Equations (VNDEs), where we review the ARCHI code for solving neutral differential equations, and then an improvement of this code will be presented in this paper to solve problems of VNDEs. This improvement is done by suggesting a new hybrid method of special interpolants with iteration procedure of RK method. We will analyze the convergence of the suggested new method by proving the following criteriaBn(t)≤TH(c1+(c2L3)/(1-L3 ))exp(LT/(1-L3 ))Where the solution is convergent , we have  and when and the derivative of the solution is also convergent for VNDEs.

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Stipan Podobnic mail -

Barbara Charchekhandra mail

link https://doi.org/10.54216/GJMSA.0110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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Galoitica: Journal of Mathematical Structures and Applications | 2024

On Some Results about Complex Order Bessel's Equation

In this paper, we derive the Bessel's equation of complex order (n + i) from the classical well-known Bessel's equation. In addition, we generalize that recurrence relation from Bessel's equation of order (n) to Bessel equation of complex order (n + i). On the other hand, we present an algorithm to solve the novel complex order equation with many illustrated examples that explain the validity of our approach.

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Arwa Hajjari mail

link https://doi.org/10.54216/GJMSA.0110102

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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Galoitica: Journal of Mathematical Structures and Applications | 2024

On Some W-Hosoya polynomials for Several Special Connected Graphs

Let u and v be any two distinct vertices in a connected graph G. A container C(u,v) is a set of internally disjoint u - v paths. The width of C(u,v) is denoted by w or w(C(u,v)), it is equal to , and the length of is the length of the longest u – v path in C(u,v). Then, for a given positive integer w, the width distance between any two distinct vertices u and v in a connected graph G is define by:dw (u,v)=min/(C(u,v)) l(C(u,v)) , where the minimum is taken over all containers C(u, v) of width w.In this paper, we find the Hosoya polynomials and Wiener indices of the join of two special graphs such as bipartite complete graphs, paths, cycles, star graphs and wheel graphs with respect to the width distance.

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Lee Xu, Taher mail -

Ahmed Jubbori mail

link https://doi.org/10.54216/GJMSA.0110103

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

A Proposed Ensemble Model of Network Intrusion Detection System for binary and Multiclassification

A network Intrusion detection system is a system that can find out different types of attacks. ANIDS is used to find out the noble type of attack by using machine learning and deep learning techniques. These techniques are very useful to find out those attacks whose patterns are not stored in the database. Therefore, these types of systems need more research to improve their accuracy and reduce the false alarm rate. In this paper, we are going to propose an ensemble framework for NIDS using different ML and DL techniques. In this paper, we have used the XGBOOST algorithm for feature extraction and for classification, CNN and RNN deep learning techniques are used. This ensemble model is used for the binary and multiclassification of attacks. Our model was checked on the dataset CICIDS-2018 which gives a better accuracy and low false alarm rate.

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Amrita Bhatnagar mail -

Arun Giri mail -

Aditi Sharma mail

link https://doi.org/10.54216/FPA.170105

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

Classification Nutrient Deficiency of Maize Plant Leaf Using Machine Learning Algorithm

The development and productivity of maize, an important crop worldwide, may be stunted by several nutritional deficiencies. If we want to increase maize output, we need to find these problems quickly. This study suggests a thorough method for identifying nutritional deficits in maize plants by analyzing leaf photos. Our approach combines deep learning algorithms with conventional machine learning methods to analyze and extract information from these pictures. The four types of nutritional deficiencies that were examined are zinc (Zn), potassium (K), nitrogen (N), and phosphorus (P). The standard machine learning method uses Gabor, Discrete Wavelet Transform, Local Binary Pattern, and Gray-Level Co-occurrence Matrix (GLCM). Then, classification is done using algorithms like Support Vector Machine (SVM), Decision Tree, and Gradient Boosting. According to our experimental data, machine-learning algorithms successfully diagnose nutritional deficits in maize plants. The results of this study highlight the promise of machine learning algorithms for improving agricultural yields via better plant nutrition management. Farmers and agricultural specialists may greatly benefit from automated image analysis that can identify nutritional deficits in maize plants quickly and correctly. This technology has the potential to contribute to the sustainability and security of food on a worldwide scale.

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Ashish Patel mail -

Richa Mishra mail -

Aditi Sharma mail

link https://doi.org/10.54216/FPA.170106

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

BBOA-SNDAE: A Deep Learning Model for HD Prediction in Medical IoT Systems

The recent progress in the Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing has revolutionized the traditional healthcare system, upgrading it into a smart healthcare system. Medical services can be enhanced by integrating essential technology such as IoT and AI. The integration of IoT and AI presents several prospects within the healthcare industry. In this research, a novel hybrid Deep Learning (DL) model called Binary Butterfly Optimization Algorithm with Stacked Non-symmetric Deep Auto-Encoder (BBOA-SNDAE) for HD (HD) prediction based on the Medical IoT technology. The key aim of the work is to categorize and predict HD utilizing clinical data with the BBOA-SDNAE model. Initially, the model is trained using the Cleveland and Statlog datasets. The input data is preprocessed and standardized utilizing the Min-Max normalization. After preprocessing, the selection of features is performed utilizing the BBOA to choose the best optimal features for improved classification. Based on the selected features, the classification is performed using the SNDAE technique. The research model was assessed based on accuracy, sensitivity, precision, specificity, NPV, and F-measure. The model attained 99.62% accuracy, 99.45% precision, 99.32% NPV, 99.56% sensitivity, 99.45% specificity, and 99.38% f-measure using the HD dataset, and the model attained 98.84% accuracy, 98.73% precision, 98.34% NPV, 98.62% sensitivity, 98.21% specificity, and 98.27% f-measure using the sensor data. The results of the research model were compared with the current model for validation, where the research model outperformed all the compared models.

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Radhika .B mail -

Noor Fathima mail -

Leelavathi .V .V mail -

Ambika .N .A mail -

Pratibha .S mail -

Asma Banu .S mail

link https://doi.org/10.54216/JISIoT.140105

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Intelligent Segmentor: Self Supervised Deep Learning based Multi Organ and Tumor Segmentation with Pseudo Lables Generation from CT Images

Multi Organ and tumor segmentation is the challenging task in medical imaging and surgical planning scenarios due to its diverse applications includes lesions and organs measurements and disease diagnosis respectively. Although collecting and examining labels for all classes pose severe challenges. Furthermore, Graphical Processing Unit (GPU) optimization emerge as another critical factor for multi organ and tumor segmentation. To address the mentioned conventional challenge, we designed a deep learning-based model named “Intelligent Segmentor” which performs automated segmentation in end-to-end fashion with novel semi supervised training approach. Initially, the obtained multi organ CT images is then subjected to pre-processing in terms of geometric standardization, noise removal, and intensity normalization respectively. The pre-processed image is then further provided to dual view training for effective Pseudolabel generation. The labelled data along with generated pseudolabels are provided to train the model for amplifying the model performance. After that, there are two inputs are provided to the designed segmentation model which includes dual encoders such as GoogleNet and VGG-16 for contextual and spatial information extraction in five stages, Tweaked Feature Pyramidal Network (TFPN) for dimensionality reduction and side features extraction, and Gated Fusion Module (GFM) for fusing the side features to form unified feature map. Finally, the unified feature map is the examined through convolution layers for multi organ and tumor output. We adopted FLARE 2023 dataset for validating the proposed work with existing works on 13 various organs and tumor segmentation tasks. From the results, the proposed research achieves better Dice Similarity Coefficient (DSC) and Normalized Surface Dice (NSD) through online validation and final testing than the existing works.

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P. Savitha mail -

Laxmi Raja mail -

R. Santhosh mail

link https://doi.org/10.54216/JISIoT.140106

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Deep Learning for Energy Forecasting Using Gated Recurrent Units and Long Short-Term Memory

Forecasting energy demand is essential for efficient grid management as it promotes steady operations, efficient markets, and sustainable energy practices. In this study, previously observed, evenly spaced energy consumption data are analysed using recurrent neural networks based on Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures to extract important insights, features, and remarkable patterns. First, the study examines the influence of meteorological features on energy consumption. The most significant meteorological features are determined by computing the MIC and Pearson's correlation coefficient. The selected features are then combined with historical energy consumption data to feed the neural network. Second, to improve and optimise the performance of the proposed models, two technical indicators - the daily energy usage average and the simple moving average - are considered. The following are some instances of comparisons in terms of prediction accuracy: (1) The MAPE of the proposed model is 2.47, whereas that of the current model is 4.03. (2) The MAPE of the existing model is 25.83, whereas the proposed solution is 18.68. (3) The MAPE of the suggested model is 24.8, while the MAPE of the current model is 26.6. (4) The MAPE of the present model is 4.77, whereas the suggested approach's is 4.42.

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E. T. Sivadasan mail -

N. Mohana Sundaram mail -

R. Santhosh mail

link https://doi.org/10.54216/JISIoT.140107

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Modelling a Constructive Approach For Predicting Attacks Over IoT Network Environment

Internet of Things (IoT) devices are more attractive towards various vulnerable activities and nodes are easily compromised by attackers. The complexity of insecure IoT node installation relies on device heterogeneity and resource constraints because of the network ends and conventional endpoints. This work concentrates on modeling an efficient IoT-based preservation model () which is a lightweight approach used for detecting anomaly and performance various analyses at the endpoints. This work integrates linear Support Vector Machine for pattern analysis and adaptive fuzzy rule model for data pattern rule generation to examine malicious network functionality and network traffic. While adopting the rules, the compromised node needs to fulfill the generated rules; when it fails then it is considered as malicious activity. Then, the models impose network access restrictions on the compromised and terminate the further process. Thus, the nodes are prevented from further network attacks. The evaluation model is done with the use of an online available network dataset and the dataset samples are evaluated in the complex network scenario. The simulation is done in MATLAB 2020a simulation environment and the accuracy attained with this model is higher compared to other approaches. Similarly, other metrics like False Alarm Rate (FAR) are evaluated for predicting malicious network functionality. The significance of the model is evaluated based on the prediction and mitigation of various network attacks.  The anticipated model shows a prediction rate of 90.21% for DoS attacks, 89.13% for R2L, 91.65% for probe, and 93.56% for U2R attacks.

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B. Sowmya mail -

Nagendra Muthuluru mail

link https://doi.org/10.54216/JISIoT.140108

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Enhancing Task Scheduling Process in Fog Computing using GTO-SSSA: A Metaheuristic Approach

Task scheduling (TS) in fog computing (FC) involves efficiently allocating computing tasks to fog nodes, considering factors such as minimizing execution time, energy consumption, and latency to meet the quality-of-service (QoS) requirements of the Internet of Things (IoT) and edge devices. Efficient TS in FC is crucial for optimizing resource usage, minimizing latency, and ensuring that IoT and edge devices receive timely and high-quality services. The growing complexity of FC environments, along with the dynamic nature of IoT applications, necessitates innovative TS models using metaheuristic algorithms to allocate tasks and meet diverse quality-of-service requirements efficiently. This research introduces the GTO-SSSA (Gorilla Troops Optimization with Skip Salp Swarm Algorithm), a novel model for intelligent TS in FC environments. This model capitalizes on the collaborative nature of the GTO algorithm while incorporating enhanced exploration and exploitation capabilities via the SSSA algorithm's skipping mechanism. The primary objective of GTO-SSSA is to tackle the intricate challenges of TS in FC effectively. This includes the efficient allocation of tasks to fog nodes, considering multiple objectives such as minimizing makespan, execution time, and throughput. The GTO-SSSA model in FC demonstrates improved efficiency, consistently surpassing compared models across various task quantities with significantly reduced makespan values. Performance improvement rates for GTO-SSSA over other models show substantial gains in TS efficiency, ranging from 0.87% to 17.83%. The model exhibits scalability as it maintains its efficiency even with an increased number of tasks, aligning with the dynamic nature of IoT applications.

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V. Arulkumar mail -

R. Lathamanju mail -

T. Nithya mail -

T. Rajendran mail

link https://doi.org/10.54216/JISIoT.140109

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new