Hesitation-Gated Fuzzy–Neutrosophic Prototype Learning under Asymmetric Label Noise

Necati Olgun; Ahmed Hatip

doi:https://doi.org/10.54216/JNFS.110103

Journal of Intelligent Systems and Internet of Things | 2024

Energy saving of cluster computing by CPU frequency Tuning using genetic algorithm

Dynamic voltage and frequency scaling (DVFS) is a tool used primarily to decrease computer processor energy consumption by lowering its operational frequency. Their only downside is that they distract from the efficiency of parallel applications while operating on parallel platforms. In a heterogeneous cluster architecture, however, a genetic algorithm is being implemented and applied to model the best trade-off between energy-saving and parallel application performance degradation. The proposed algorithm selects the best frequency vector in order to accomplish these objectives by providing the same compromise. So, the objective function of the genetic algorithm at the same time gives limited energy consumption and minimum decreases in performance. The SimGrid simulator will be used for all experiments. The suggested algorithm saves the average energy by (20 %) and the application performance degrades to the limit (0.15 %).

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Zainab A. Abdulazeez mail -

Nihad Abduljalil mail -

Ahmed B. M. Fanfakh mail -

Ali Kadhum M. Al-Qurabat mail -

Esraa H. Alwan mail

link https://doi.org/10.54216/JISIoT.130210

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

An effective Web System for Weather monitoring using Artificial Neural Network Based on Internet of Things and Cloud Computing

This research presents an effective web system for weather monitoring based on the Internet of Things (IoT) and cloud computing. The three primary parts of the system are an online application, a cloud platform, and Internet of Things-based weather stations. Periodically, IoT-based weather stations gather meteorological data and send it to a cloud platform. The ANN model can access the meteorological data that is stored in a database by the cloud platform. ANN model utilizes the meteorological data to produce predictions for several weather factors. The web application gives users access to real-time weather data and forecasts.

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Ebtehal Akeel Hamed mail -

Nahla Ibraheem Jabbar mail -

Zaid Th Hassan mail -

Refed Adnan Jaleel mail

link https://doi.org/10.54216/JISIoT.130211

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Enhanced Anomaly Detection in IoT Networks Using Hybrid Deep Learning and Bio-Inspired Optimization

The rapid expansion of Internet of Things (IoT) devices has significantly amplified cybersecurity risks, thereby necessitating advanced anomaly detection mechanisms. This research introduces a hybrid detection framework tailored for IoT networks, combining deep learning architectures with bio-inspired optimization techniques. At the core of the framework lies the IoT Autoencoder-Based Feature Extraction Network (IoTAE-FEN), designed to minimize data dimensionality while preserving key discriminative features. To further refine the selected attributes, a Binary Multi-Objective Enhanced Gray Wolf Optimization (BMOEGWO) strategy, modeled on the cooperative hunting behavior of gray wolves, is employed. For the classification phase, Random Forest (RF) is integrated, resulting in the proposed AE-BMOEGWO-RF hybrid model. The effectiveness of this approach was validated on benchmark datasets, including NSL-KDD and TON-IoT. Experimental findings highlight a feature selection accuracy of 96.85% on the TON-IoT dataset and an overall classification performance of 97.81% on NSL-KDD. Comparative evaluations against existing techniques underscore the framework’s superior detection capability, emphasizing its potential to strengthen IoT network security by addressing longstanding challenges in feature extraction and selection for anomaly detection.

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M. Sindhuja mail -

Noorfazila Kamal mail -

Kalaivani Chellappan mail

link https://doi.org/10.54216/JISIoT.170224

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Software Testing Using Cuckoo Search Algorithm with Machine Learning Techniques

Software testing are any errors, flaws, bugs, mistakes, failures in a piece of software that might cause the programme to produce incorrect or unexpected results. Testing in software almost always increase both the time and money needed to finish a project. And finding bugs and fixing them is a laborious and expensive software process in and of itself. While it's unrealistic to expect to completely eradicate all testing from a project, their severity may be mitigated. It is possible to predict where bugs may appear in software using a method known as software defect prediction (SDP). The goal of each software development project should be to provide a bug-free product. Predicting where bugs may appear in code, often known as software defect prediction (SDP), is an important part of fixing software. Software of a high calibre should have few bugs. A software metric is a quantitative or qualitative evaluation of some aspect of the programme or its requirements. One of the more recent population-based algorithms, Cuckoo Search (CS) was inspired by the flight patterns of some cuckoo species as well as the Lévy flying patterns of other birds and fruit flies. The needs for international convergence are met by CS. KNN is a significant non-parameter supervised learning technique. This paper presents an overview of Stochastic Diffusion Search (SDS) in the form of a social metaphor to illustrate the processes by which SDS allots resources. The best-fit pattern identification and matching difficulties were addressed by SDS using a novel probabilistic method. As a multiagent population-based global search and optimization method, SDS is a distributed model of computing that makes use of interaction amongst basic agents. The behaviour of SDS is described by studying its resource allocation, convergence to global optimum, resilience, minimum convergence criterion, and linear time complexity within a rigorous mathematical framework, setting it apart from many nature-inspired search algorithms. This paper proposes a hybrid optimization strategy based on CS-SDS techniques. By using the global search strategy solution of the SDS algorithm, this hybridization idea aims to enhance the cuckoo bird's search strategy for the optimum host nest. To that end, the SDS method would be used to place the cuckoo egg in the most advantageous location. When compared to other classifiers, PC2's improved performance may be attributed to its higher recall values. When compared to the Naive Bayes and Radial Bias Neural Network classifiers, the KNN performs 7.64% and 2.20% better, respectively.

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Deepashree N mail -

M. Sahina Parveen mail

link https://doi.org/10.54216/JISIoT.130212

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

Transfer Learning and Optimised Firefly Neural Network for Lung Cancer

Today's clinical analysis and precise illness detection are mandated requirements for the development of intelligent expert systems. Since lung cancer affects both men and women equally and has a greater mortality rate than other illnesses, a more complete examination is needed to diagnose lung cancer. More helpful information regarding a lung cancer diagnosis may be provided by images from a computer tomography (CT) scan. Various machine learning and deep learning algorithms are created to enhance the medical treatment process using CT scan input pictures. But research still has a bad side when it comes to creating a precise and intelligent system. In order to improve the detection of lung tumors from the CT input images, this paper presented Firefly optimized pre trained transfer learning. The previously trained model VGG-16 is used in this paper to extract features more effectively, using the features chosen via the firefly optimization approach to increase classification accuracy while reducing complexity. The thorough testing done with the “LUNA-16 & LIDC Lung image” datasets is assessed & studied along with other performance measures like "accuracy, precision, recall, specificity, and F1-score". Investigation results show that the suggested design outperformed the “DenseNet, AlexNet, Resnets-50, Resnets-100, VGG-16 & Inception models” and reached the top results with "98.5% accuracy, 99.0% precision, 98.8% recall, with 99.1% F1-score.

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A. Gopinath mail -

P.Gowthaman mail

link https://doi.org/10.54216/JISIoT.130213

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2024

LoRa Architecture-Enabled Intelligent for Agriculture with Deep Learning Architecture

The agricultural industry faces significant challenges in improving efficiency and productivity, particularly in monitoring crop health and environmental conditions. Traditional methods are often labor-intensive, time-consuming, and lack real-time data, leading to suboptimal decision-making. Recent advancements in Internet of Things (IoT) and Artificial Intelligence (AI) technologies offer promising solutions. Long Range (LoRa) communication, a type of low-power wide-area network (LPWAN), enables long-distance data transmission with minimal power consumption, making it ideal for rural and expansive agricultural areas. When combined with deep learning, which can analyze large volumes of data to generate predictive insights, these technologies have the potential to revolutionize agricultural practices by providing farmers with timely and accurate information to optimize crop management and resource utilization. This study introduces an intelligent mote for agricultural applications, leveraging Long Range (LoRa) communication and deep learning techniques to improve precision farming. Traditional agricultural monitoring methods are labor-intensive and lack real-time insights. To address this, the mote is equipped with sensors to monitor temperature, humidity, soil moisture, and light intensity, transmitting real-time data over long distances with minimal power consumption using LoRaWAN. The collected data is processed by deep learning models to predict crop yield and identify potential issues. Field tests demonstrated a 15% improvement in yield prediction accuracy and a 20% reduction in water usage compared to traditional methods. These results highlight the effectiveness of integrating LoRa and deep learning in enhancing agricultural resource management and productivity.

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K M Monica mail -

Anitha D mail -

S.Prabu mail -

B.Girirajan mail -

Arun M mail

link https://doi.org/10.54216/JISIoT.130214

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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American Journal of Business and Operations Research | 2024

An Examination of the Link between Organizational Culture and Strategy Formulation in a Selection of Iraqi Private Universities

This study aims to investigate the connection between organizational culture and strategy formulation in several private colleges in Iraq, as organizational culture is a major factor in the success or failure of organizations, and it is a crucial element in organizational transformulation and growth, which is a characteristic of the modern age. This research seeks to explore the significance of organizational culture by looking at its resurgence, its cultural makeup, and the dimensions of strategy formulation in universities and private colleges. It will then examine the connection between organizational culture and strategy formulation among the study sample. The hypothesis is that there is no meaningful relationship between organizational culture and strategy formulation. The research sample of (100) lecturers from (10) private universities and colleges was surveyed using a questionnaire to assess the interest in organizational culture in the educational community. The results revealed that there is a relative interest in the culture, but it is not given an important role in formulating the strategy. It was suggested that mental and intellectual abilities and experiences should be harnessed through dialogue and direct training to transform them into a powerful tool for formulating educational strategy.

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Ahmed Abdel Qader Ismail Alnajem mail

link https://doi.org/10.54216/AJBOR.110201

Volume & Issue

Vol. Volume 11 / Iss. Issue 2

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International Journal of Artificial Intelligence and Education Technology | 2027

SmartTutor-GPT: An AI-Driven Intelligent Tutoring System Using ChatGPT for Enhancing Personalized Learning in Online Education Platforms

In the current era, the rapid evolution of artificial intelligence has opened doors to combining education with cutting-edge AI capabilities. However, prominent online learning platforms like Coursera and Udemy have not yet tapped into this synergy. This paper proposes an intelligent tutoring system that leverages AI innovations such as ChatGPT to enhance the online learning experience and improve student performance. To achieve this, the paper use the OpenAI interface to interact with ChatGPT, employing a suite of frameworks including React, Spring Boot, and Flask for a seamless front-end and back-end service delivery. On the infrastructure front, the robust capabilities of AWS, Kubernetes, Docker, and Jenkins is employed to facilitate continuous integration and continuous deployment (CICD). To assess the effectiveness of our system, we use a combination of A/B tests, tree tests, and questionnaires. These methods are complemented by stress tests and monitoring mechanisms to ensure reliability. While the proposed system satisfied the majority of functional and non-functional requirements, the experimental group demonstrated greater learning gains than the control group, with average scores increasing from 0.50 to 3.33 (improvement = 2.83), compared with 0.66 to 3.00 (improvement = 2.33) for the control group. The lower variance in the experimental group (0.333 vs. 1.00) indicates more consistent outcomes; however, the t-test results suggest that the difference in learning outcomes was not statistically conclusive.

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Alcardo Barakabitze mail

link https://doi.org/10.54216/IJAIET.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

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Journal of Neutrosophic and Fuzzy Systems | 2026

Robust Jensen–Shannon Consensus Geometry for Single-Valued Neutrosophic Information

Let x = (T, I,F) ∈ [0,1]3 denote a single-valued neutrosophic assessment and let wT +wI +wF = 1 with wc > 0. This paper introduces the probability-completed embedding Φw(x) = 􀀀 wT T,wT (1−T),wI I,wI(1−I), wFF,wF (1−F) ∈ Δ5. and the pullback distance dw(x,y) = [J(Φw(x),Φw(y))/log2]1/2 , where J is Jensen–Shannon divergence. The construction yields a bounded metric, separates into three weighted Bernoulli Jensen–Shannon terms, is invariant under the neutrosophic complement xc = (F,1−I,T) when wT = wF , and has the local information metric d2w (x,x+δ) = 1 8log2 Σ c∈{T,I,F} wcδ2 c xc(1−xc) +O(∥δ∥3). On this geometry, robust consensus is posed as the bounded M-estimation problem bxτ = argmin x∈(0,1)3 mΣ r=1 ar{1−e−τd2w (x,xr)}. A majorization–minimization iteration reduces each step to three one dimensional weighted Jensen–Shannon barycenters and decreases the objective monotonically. The induced expert weight is proportional to e−τd2w, so strongly conflicting assessments are downweighted without a hard rejection threshold. In a reproducible contamination study with 15 experts, 800 replications at each of five contamination levels, and a fixed τ = 60, the proposed estimator has mean normalized Jensen–Shannon error 0.0149 at 40% oppositional contamination; the coordinate median, ordinary Jensen–Shannon barycenter, and arithmetic mean obtain 0.0380, 0.1448, and 0.1489, respectively. The contribution is therefore a metric and optimization framework for consensus itself, rather than another ranking operator: neutrosophic disagreement is represented on a common information-geometric scale and robust aggregation follows from a bounded variational principle.

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Nabil Salman mail -

Rozina Ali mail

link https://doi.org/10.54216/JNFS.110102

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new

Journal of Neutrosophic and Fuzzy Systems | 2026

Hesitation-Gated Fuzzy–Neutrosophic Prototype Learning under Asymmetric Label Noise

Label corruption is difficult for prototype classifiers because a mislabeled observation does two things at once: it perturbs the prototype vyi associated with the supplied label and obscures whether the observation is genuinely ambiguous or simply inconsistent with its assigned class. This paper develops an adaptive fuzzy–neutrosophic prototype learning algorithm that separates these effects. For each training observation, the membership vector ui = (ui1, . . . ,uiK) ∈ ΔK−1 induced by the current prototypes is converted into the evidence state zi = (Ti, Ii,Fi) ∈ [0,1]3: truth is the membership assigned to the observed class, falsity is the strongest competing membership, and indeterminacy is the normalized membership entropy. These quantities drive three coupled mechanisms: a contradiction margin ci = [Fi −Ti]+ that attenuates unreliable labels, an entropy-dependent fuzzy exponent mi ∈ [mmin,mmax] that adapts membership weighting near class overlap, and a conservative soft-label correction activated only when Fi > Ti and the hesitation Ii is sufficiently small. The resulting Adaptive Fuzzy–Neutrosophic Prototype Learning (AFNPL) algorithm remains a lightweight prototype method with linear cost in the number of observations, classes and features per iteration. A reproducible three-class study evaluates 0–40% cyclic asymmetric label corruption under low, medium and high class overlap. At medium overlap and 40% corruption, AFNPL obtains 91.54% test accuracy and 91.55% macro-F1, compared with 72.52%/72.37% for noisy class means and 80.23%/80.17% for trimmed class means. Its internal contradiction score also detects corrupted labels with mean AUC between 0.959 and 0.971 across the contaminated settings. The contribution is therefore not only a robust prototype update, but a fuzzy learning mechanism in which neutrosophic truth, indeterminacy and falsity have explicit algorithmic roles in label reliability and adaptive fuzzy weighting.

groups

Necati Olgun mail -

Ahmed Hatip mail

link https://doi.org/10.54216/JNFS.110103

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new