An Intelligent Framework for Flavor Recommendation and Cost Optimization in Hybrid Cloud Autoscaling

Agnes Osagie; Sandra Terazic; Barbara Charchekhandra

doi:https://doi.org/10.54216/JCHCI.090207

Journal of Cognitive Human-Computer Interaction | 2025

Detecting Cyberbullying and Hate Speech in Regional Languages Using Hybrid Deep Learning and NLP Models

The rise of social media platforms has led to an increase in cyberbullying and hate speech, which can have severe consequences on individuals and communities. The detection of harmful content, especially in regional languages, remains a significant challenge due to the linguistic diversity, informal expressions, and limited datasets available for training machine learning models. This paper proposes a hybrid deep learning and natural language processing (NLP) model for the detection of cyberbullying and hate speech in regional languages. The model combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) with advanced NLP techniques such as sentiment analysis and context-aware feature extraction. Preliminary experiments show that the proposed model achieves an accuracy of 86.7% for hate speech detection and 82.3% for cyberbullying detection in regional language datasets. Furthermore, the hybrid model outperforms traditional machine learning techniques by 15% in terms of precision and recall. This approach demonstrates the potential of combining deep learning and NLP to address the challenges of detecting harmful content in diverse linguistic environments.

groups

Ganesh C. mail -

Kumarganesh S. mail -

Elayaraja P. mail -

Thiyaneswaran B. mail

link https://doi.org/10.54216/JCHCI.090206

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2025

Nature-Inspired Metaheuristic Optimization for Network Design and Communication Systems: Trends, Applications, and Future Directions

Communication network design (CND) and wireless sensor networks (WSNs) presents significant challenges, particularly in optimizing network reliability, energy efficiency, and cost-effectiveness. This literature review discusses using metaheuristic algorithms to solve the mentioned NP-hard problems and provide accurate results for network reliability, resource management assignment, and energy utilization in data transmission networks. Recent advancements in hybrid metaheuristic approaches, such as combining Genetic Algorithms with Branch and Bound (GA-BB) or Particle Swarm Optimization with Simulated Annealing (PSO-SA), demonstrate their effectiveness in optimizing network performance in emerging domains like vehicular ad-hoc networks (VANETs) and Internet of Things (IoT)- enabled WSNs. The review also discusses the application of optimization approaches about distinct issues such as cluster head selection in WSNs, routing protocols in dynamic networks, and supply chain network design. These developments are essential in evolving technologies such as 6G networks and the Internet of Everything (IoE), where complex systems demand innovative optimization strategies. By highlighting these concerns in the current study, this review calls for the increased use of metaheuristic techniques towards furthering the application of future networks in smart cities, healthcare, and secure network architectures.

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Hussein Ibrahim Hussein mail -

Lateef Abd Zaid Qudr mail -

Weal Hasan Ali Almohammed mail

link https://doi.org/10.54216/JAIM.090107

Volume & Issue

Vol. Volume 9 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic Statistical Analysis on Healthcare Data for Oral Cancer Detection

Oral cancer is presently a growing health concern at the global level, with intense incidences of lifestyle factors. The increasing mortality rates of the diseased shall be controlled with effective early detection mechanisms. However, the traditional statistical approaches in practice fail to deliver in making a precise diagnosis of this cancer due to the intricate and interdependent prevalence of symptoms. This research work provides a solution approach using the potency of neutrosophic statistics in developing neutrosophic-integrated models of random forests and decision trees. Neutrosophic representation of data considering the indeterminacy, values of truth, and falsity facilitates healthcare experts in handling the conflicting patient data. The proposed random forest decision model with neutrosophic logic identifies the significant features, and the neutrosophic decision tree classifier predicts the stages of cancer. The findings are compared with conventional modelling of random forest and decision trees, and it demonstrates the efficiency and precision of neutrosophic statistical analysis in predicting oral cancer. This proposed neutrosophic decision framework will assist and support the medical practitioners and research experts in gaining more insights and deeper comprehension of the cancer progression and suggesting suitable treatment plans to minimize the morbidity rate.

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Sakshi Taaresh Khanna mail -

Sunil Kumar Khatri mail -

Neeraj Kumar Sharma mail

link https://doi.org/10.54216/IJNS.260323

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

CBi-BERT: Efficient Skin Disease Image Segmentation Using Patch-Based Deep Feature Mapping and BERT-Based Attention Mechanism

Skin image segmentation serves as a vital undertaking in medical image analysis, specifically in dermatology, since it enables the detection of skin diseases and the assessment of effectiveness of treatments. Segmenting skin lesions from photographs is a crucial step in working towards this patchive. Nevertheless, the work of segmenting skin lesions is difficult due to the existence of both artificial and natural deviations, inherent characteristics like the shape of the lesion), and deviations in the circumstances during which the images are obtained. In recent years, researchers have been investigating the feasibility of utilizing deep-learning models for skin lesion segmentation. Deep learning methodologies have exhibited encouraging outcomes in various image segmentation initiatives, thereby preventing the possibility of automating and enhancing the precision of skin segmentation. This paper introduces a new hybrid method, named the CBi-BERT framework, aimed to improve the results and architectures of medical image segmentation or patch detection tasks. This architecture employs Convolutional Neural Networks (CNNs) for feature extraction as well Bidirectional LSTM-based encoders to process sequence information and BERT based attention collection across the strongest features. Image normalization, resizing and data augmentation techniques are applied in the proposed method to deal with major challenges faced during medical imaging such as rate of image quality differentiation from noise or bias between benign vs. malign features. We evaluate the performance of CBi-BERT to those benchmark datasets and state-of-the-art models, showing that CBi-BERT outperforms them in all relevant metrics such as Intersection over Union (IoU), recall, mean average precision (bin-MAP) DICE coefficient. Specifically, for images sized 256x256 the model achieved IoU =0.9, recall=0.92, mAP=0.89 and Dice coefficient: =0.91 thereby outperforming some advanced state-of-the-art models as ResNet50, VGG16, UNet, EfficientNet-B-01 Our results show that the framework is able to detect and segment important structures in medical images with high precision which makes it a compelling tool for AI based Healthcare solutions.

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Summi Goindi mail -

Khushal Thakur mail -

Divneet Singh Kapoor mail

link https://doi.org/10.54216/JISIoT.170117

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

A Study on NCT-Filters in NCT-Topological Spaces

In this research, we introduce and develop new concepts in the field of Neutrosophic Topology (NCT). Particularly our study is focusing on the filter and its properties. Also, we present the properties of convergence of -filter, a specialized filter that incorporates neutrosophic values, providing a robust approach to handle uncertainty in topological spaces. Additionally, we explore the concept of adherent points in neutrosophic crisp triple topological spaces, offering a new perspective on the study of these spaces. Moreover, our findings contribute to expanding the understanding and application of neutrosophic theories in topology that will provide a solid foundation for future research in this area. Furthermore, this work opens new avenues for the study of topological spaces under uncertainty, with potential Applications in various domains, including data analysis, decision-making, and artificial intelligence, among others.

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Dheargham Ali Abdulsada mail -

Audy Hatim Saheb mail -

Rasheed Al-Salih mail -

Mohammed Hadi Lafta mail

link https://doi.org/10.54216/IJNS.260324

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Computational Approaches for Nonlinear Fractional Differential Problems Utilizing Chebyshev Polynomial Approximations Space with Neutrosophic Applications

Applying Chebyshev polynomial approximate results, this paper applies the idea of neutrophilic logic to the approach to partially differential equations (FPDEs).  Three elements make up the Neutrosophic technique: Indeterminacy (I), Falsehood (F), and Truth (T).  These three elements are appropriate for issues where precise values or distinct limits are lacking since they are utilized to represent ambiguity, vagueness, and imperfect truth in mathematical models.  We improve the depiction of real-world occurrences that could contain unclear or ambiguous information by adding these values to the coefficients of FPDEs.  In domains like material science, mechanical engineering, and biological phenomena, where uncertainty is inevitable, the use of neutrophilic logic enables a more thorough and precise approximation of approaches to complicated fractional differential equations. The findings show that when working with systems that have unknown characteristics, the Neutrosophic technique increases the accuracy and dependability of computations.

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Abdulsalam Al-Dulaimi mail -

Amirah Azmi mail -

Yaseen S. R. mail

link https://doi.org/10.54216/IJNS.260325

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

On Class of Bi-univalent functions involving Neutrosophic 𝓆-Poisson distribution Series

This paper introduces and investigates a new class of bi-univalent functions constructed through the Neutrosophic 𝓆-Poisson distribution series. The study focuses on estimating the upper bounds of the basic coefficients |a_2 |and |a_3 |   in the Taylor series expansion of these functions.

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Banin Shaker Jubeir mail -

Mohammad El-Ityan mail -

Rafid Habib Buti mail -

Mohammed Hassan Hamza mail

link https://doi.org/10.54216/IJNS.260326

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Journal of Sustainable Development and Green Technology | 2025

Sustainable Economic Development and Principles of Green Economy of Uzbekistan in 2017–2024

The study examines Uzbekistan’s economic development and the implementation of green economy principles from 2017 to 2024. During this period, the country achieved notable progress in economic diversification, the adoption of renewable energy sources, and ensuring environmental sustainability. Nevertheless, challenges such as waste management and the efficient use of water resources remain pressing issues that require future attention and resolution.

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Khodjieva Dilrabo mail

link https://doi.org/10.54216/JSDGT.050103

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2025

Data-Driven Pricing Decisions for Ensuring the Success of Strategic Product Development

By effectively including diffusion into the framework, this study further illustrates whether Optimal Control Theory may be used to identify and address the control of prices issue of technology items. There is a three-stage paradigm that it uses to describe the procedure of adoption process: consciousness, inspiration, and adopting itself. The process is described by the diffusion logistic functions; furthermore, the model takes into account price fluctuations’ sensitivity. The selling price is a choice variable constrained such that the total profit over the relevant planning horizon is optimised. In this paper, the Hamiltonian function is used in obtaining necessary optimality conditions with spectral learning effects and Costate equations complemented by the adoption rates by use of Pontryagin’s Maximum Principle. As the problem is formulated as the continuous optimization problem, it is discretized for its practical applications, and the model is solved with the help of LINGO 15.0 software. The data used to validate the model implemented was derived from historical sales records of the electronics and semiconductor industries to obtain a measure of realism. Analysed sensitivity studies show how variations in adoption parameters including the price elasticity and customer attrition affect adoption rates and profitability. As such, the study offers managerial implications for the management of private sector schemes to focus on the application of dynamic pricing strategies as the optimal balance between consumers’ perceived value and firm revenues. It provides managers with strong tools for the implementation of adoption into a new generation of technology-enabled markets, maximization of revenues, and sustaining of competitive advantage. Outperforming all analysed models, the suggested technique employing Optimal Control Theory obtains an accuracy of 96%. This proves that the suggested strategy is the best at forecasting when a product will be adopted.

groups

Ramazan Yasar mail -

Sergey Drominko mail

link https://doi.org/10.54216/AJBOR.120205

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

An Intelligent Framework for Flavor Recommendation and Cost Optimization in Hybrid Cloud Autoscaling

This research presents a flavor recommendation framework that intends to be used in hybrid clouds to address resource provisioning and cost issues. A cloud “flavor” is an instance type that assigns values for CPU, memory, storage, and networking. Today the flavor selection process is manual, and no dynamic technique is used, therefore, the process is inefficient because some flavors are underutilized. The proposed framework also provides flavor recommendations for autopiloted dynamic capacity provisioning using predictor analysis of workload and cost proportional to different CSPs. It uses an RNN_LSTM-based Proactive Predictive Engine (PPE) to quantitatively estimate future resource requirements and a Recommendation Engine consisting of the scoring and flavor engines. This framework receives the application’s actual and predicted consumption of CPU and memory, cost fluctuations, and CSPs’ options and then the selection of various flavors is performed in the runtime. Metrics are gathered, stored, and analyzed in real-time through Telegraf, InfluxDB, and Apache Libcloud for current resource allocation. Experimental results of the system on AWS and OpenStack show the benefit of using the proposed framework, which reduced the number of EBS and VMs by 19% and the cost saving by up to 17% compared with traditional and reactive approaches. This solution turns static resource allocation into a real-time predictive accuracy of how resources are best utilized as well as the expense at the hybrid cloud environment.

groups

Agnes Osagie mail -

Sandra Terazic mail -

Barbara Charchekhandra mail

link https://doi.org/10.54216/JCHCI.090207

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new