On The Computational Properties of 3-Cyclic and 4-Cyclic Refined Matrices and the Diagonalization Algorithm

Hasan; Mohammad

doi:https://doi.org/10.54216/IJAACI.060204

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

Details open_in_new

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

Details open_in_new

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

Details open_in_new

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.

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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

International Journal of Neutrosophic Science | 2025

A Note on Two-Fold Neutrosophic and Fuzzy Topological Space Based on Real Numbers

The objective of this paper is to introduce for the first time the concept of two-fold neutrosophic and fuzzy topological space defined over real numbers, where we combine the two-fold neutrosophic sets with real numbers to get a novel topological space based on them. Also, we present many of its elementary properties and special subsets such as two-fold neutrosophic open sets, two-fold neutrosophic closed sets, and two-fold neutrosophic closure. Many examples and theorems will be provided to clarify the validity of our approach.

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Rabaa Al-Maita mail

link https://doi.org/10.54216/IJNS.250137

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Towards Efficient Hyperspectral Object Detection and Classification using Thermal Optimization Algorithm with Deep Learning

Object detection in remote sensing images (RSI) is a main procedure where the purpose is to automatically recognize and categorize certain objects or features from large-scale, remotely developed images like aerial imagery or satellite. This task role a vital play in extracting appreciated data from massive geographical regions, contributing to various applications under several domains namely environmental monitoring, urban planning, agriculture, and disaster management. Recent developments in deep learning (DL) technologies have significantly enhanced the accuracy and efficacy of object detection systems for RS, enabling more precise and automated analysis of various landscapes and facilitating informed decision-making. DL approaches namely convolutional neural networks (CNNs) are exposed to remarkable abilities in learning intricate patterns and features from difficult spatial data, resulting in enhanced accuracy and effectiveness. In this article, we present a Towards Efficient Hyperspectral Object Detection and Classification using Thermal Optimization Algorithm with Deep Learning (HODC-TOADL) system. The objective of HODC-TOADL algorithm is to identify and categorize distinct types of objects that exist in the RSI. In the HODC-TOADL method, an improved Dense Net model is applied to learn the distinct features of the input RSI. Besides, the TOA has been deployed to boost the hyper parameter choice of the Dense Net method. Furthermore, the classification of objects can be carried out by employing of adaptive neurofuzzy inference system (ANFIS). The experimental evaluation of the HODC-TOADL algorithm can be studied on benchmark databases. The experimental values stated that the HODC-TOADL algorithm reaches effective classification performance compared to recent DL models.

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Noor Edin Rabeh mail

link https://doi.org/10.54216/IJAACI.060201

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Deep Learning Driven Automated Red Palm Weevil Detection Using Sparrow Search Optimization

In recent decades, Red Palm Weevils (RPW) have been demonstrated as a harmful pest of palm trees worldwide, predominantly in the Middle East. The RPW is produced massive damage to several palm varieties. Primary detection of the RPW is a complex problem to optimum date production while the recognition is avoided by palm trees as to be influenced by RPW. Several studies are driven to determine a precise approach for the detection, localization, and classification of RPW pests. Employing computer vision (CV) technology with pattern detection is verified that further productive once utilized for identifying and classifying insects. Thus, the automated method decreases either the problem or labor effort required for enhancing the farmer's income. The farmers can be stimulated to enhance the productivity of date fruit once this has been done. With this motivation, this article focuses on the design of automated RPW pest detection using sparrow search optimization with deep learning (RPWPD-SSODL) technique. The presented RPWPD-SSODL algorithm mostly focused on the detection and classification of RPW using computer vision approaches. To accomplish this, the RPWPD-SSODL technique employs bilateral filtering (BF) for noise removal. Next, the RPWPD-SSODL technique uses Dense-RefineDet object detector with ShuffleNet model as a backbone network. For improving the recognition solution, the hyperparameter tuning of the ShuffleNet model can be optimally adjusted using the SSO algorithm. To validate the simulation results of the RPWPD-SSODL technique, a wide-ranging simulation outcome is implemented. The simulation values potrayed the improvement of the RPWPD-SSODL algorithm over other approaches under several measures.

groups

Narek Badjajian mail -

Warshine Barry mail

link https://doi.org/10.54216/IJAACI.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024

Algorithms for Cybersecurity in CAVs Based On Deep Learning and Their Applications

This paper is concerned with the study of some novel techniques that using artificial intelligence to protect networks of CAVs from cyberattacks, where we use some machine learning algorithms to detect attacks and compare the machine learning algorithms used for this in terms of accuracy and required operating time. Also, WEKA tool will be used for the desired comparison, as the experiments are carried out on a new dataset, which is a dataset abbreviated from the KDD99 dataset.

groups

Sara Sawalmeh mail

link https://doi.org/10.54216/IJAACI.060203

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2024