Neutrosophic–Plithogenic IADOV for Capturing Subjective Meaning in Qualitative Research

Carmen Marina Méndez; Josía Jeseff Isea; Danny Mauricio Sandoval; Roberth Alexander Anamá

doi:https://doi.org/10.54216/IJNS.260328

Journal of Intelligent Systems and Internet of Things | 2025

Integrating Visual Sentiment Analysis with Textual Data for Enhanced Social Media Insights

Social media platforms have become pivotal arenas for the public to express emotions, opinions, and sentiments. While traditional sentiment analysis methods predominantly focus on textual data, they often overlook the rich emotional context embedded in images shared alongside posts. This paper presents a novel framework that integrates Visual Sentiment Analysis (VSA) with Natural Language Processing (NLP) techniques to enhance the understanding of public sentiment in social media content. By leveraging deep learning-based feature extraction from images (using pre-trained CNN models) and combining them with transformer-based text analysis (such as BERT), the proposed multimodal sentiment analysis model captures nuanced emotional expressions more effectively than unimodal approaches. Experiments conducted on benchmark datasets, including Twitter and Instagram posts, demonstrate a significant improvement in sentiment classification accuracy and contextual interpretation. The study highlights the potential of integrated sentiment analysis systems in applications such as brand monitoring, political opinion tracking, and mental health detection.

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M. Sivasankar mail -

K. Murugan mail -

P. Gouthami mail -

G. Balambigai mail -

Kalaivani T. mail

link https://doi.org/10.54216/JISIoT.170127

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Real-Time Gesture Recognition Using Attention-Based CNN-RNN Framework for Human-Robot Interaction

Gesture recognition serves as a key enabler for natural and intuitive human–robot interaction (HRI) in smart automation and assistive systems. However, achieving real-time performance with high recognition accuracy remains a significant challenge due to dynamic background variations, occlusion, and complex spatio-temporal dependencies in gesture sequences. This paper presents a real-time attention-based CNN-RNN framework for robust gesture recognition and adaptive HRI in dynamic environments. The proposed system utilizes Convolutional Neural Networks (CNNs) for spatial feature extraction from sequential video frames and Bidirectional Recurrent Neural Networks (BiRNNs)—integrated with an attention mechanism—for modeling temporal dependencies and focusing on discriminative motion cues. The attention layer enhances interpretability by prioritizing salient gestures and reducing background noise. A hybrid optimization strategy, combining adaptive learning rate scheduling and regularized dropout, ensures computational stability and generalization across gesture datasets. Experiments conducted on benchmark datasets such as NVIDIA Dynamic Gesture (NvGesture) and ChaLearn IsoGD demonstrate superior performance, achieving an accuracy of 97.8% and a real-time inference speed of 34 FPS, outperforming baseline CNN, 3D-CNN, and LSTM architectures. The proposed framework effectively balances accuracy, latency, and interpretability, making it suitable for real-world HRI applications, including service robotics, industrial automation, and assistive technologies.

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R. Poorni mail -

Chinnathambi Kamatchi mail -

Y. Dharshan mail -

K. Kowsalya mail -

R. Vijay mail -

M. Balakrishnan mail

link https://doi.org/10.54216/JISIoT.170128

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Emotion-Aware Recommendation Systems: Deep Sentiment Modeling for Consumer Behavior Understanding

Traditional recommendation systems primarily rely on user behavior, ratings, and content-based preferences to suggest products or services. However, they often overlook the nuanced emotional context that significantly influences consumer decision-making. This paper proposes a Sentiment-Enhanced Recommendation System (SERS) that integrates sentiment analysis with collaborative and content-based filtering to better capture the affective dimensions of user preferences. By analyzing user-generated content such as reviews, comments, and social media posts using deep learning-based sentiment classifiers, the proposed model quantifies emotional polarity and intensity. These sentiment signals are then incorporated into the recommendation pipeline using hybrid matrix factorization and attention mechanisms, enabling dynamic adaptation to users' emotional states. Experimental evaluations conducted on datasets from Amazon and Yelp demonstrate significant improvements in precision, recall, and user satisfaction scores compared to traditional models. The findings highlight the critical role of emotions in shaping consumer behavior and underscore the importance of affect-aware personalization in modern recommendation systems.

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N. B. Mahesh Kumar mail -

Subbulakshmi M. mail -

T. Baranidharan mail -

Mohana Sundharam M. mail -

Geetha M. P. mail

link https://doi.org/10.54216/JISIoT.170226

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Adaptive Image Enhancement Using Hybrid Deep Learning and Traditional Filtering Techniques

Image enhancement remains a fundamental challenge in computer vision, particularly in scenarios involving low contrast, uneven illumination, and noise interference. While traditional spatial and frequency domain techniques efficiently address specific distortions, they often fail to generalize across diverse image conditions. To overcome these limitations, this paper proposes an Adaptive Hybrid Image Enhancement Framework that integrates deep learning-based enhancement networks with classical filtering algorithms for optimal visual restoration and detail preservation. The proposed method employs a Convolutional Neural Network (CNN) enhanced with an attention-guided residual block to learn fine-grained illumination patterns, followed by adaptive fusion with traditional filters such as Gaussian smoothing, histogram equalization, and bilateral filtering. This hybrid approach ensures a balance between structural clarity and natural color consistency. A dynamic weighting mechanism is applied to adjust enhancement intensity based on local luminance and texture statistics. Experimental validation on benchmark datasets such as MIT-Adobe FiveK, BSD500, and LIME demonstrates significant improvement over state-of-the-art methods. The proposed hybrid model achieves an average PSNR of 32.8 dB, SSIM of 0.95, and naturalness index improvement of 18%, outperforming standalone deep learning and filtering techniques. The adaptive framework effectively enhances visibility in underexposed, blurred, and noisy conditions, making it ideal for applications in medical imaging, autonomous vision, and surveillance systems.

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Karthikram Anbalagan mail -

Ravikanth Garladinne mail -

K. Ananthi mail -

M. Jeba Paulin mail -

Vairaprakash Selvaraj mail -

Jayalalakshmi G. mail

link https://doi.org/10.54216/JISIoT.170227

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Neuromorphic VLSI Accelerator for Edge-Aware AI Processing Using Hybrid Spiking Neural Architectures

The rapid proliferation of edge-AI systems in IoT, autonomous robotics, and biomedical monitoring demands ultra-low-power, latency-aware intelligence that conventional deep neural networks struggle to provide due to heavy computation and memory overheads. Neuromorphic computing offers a promising biological-inspired alternative by processing information through sparse spiking events, enabling energy-efficient on-device learning and inference. This paper presents a neuromorphic VLSI accelerator based on a hybrid spiking neural architecture that combines Leaky-Integrate-and-Fire (LIF) neurons, adaptive threshold spiking units, and synaptic plasticity circuits to support both supervised and unsupervised learning modes at the edge. A hierarchical crossbar-memory topology integrated with non-volatile memristive synapses provides dense weight storage and real-time synaptic updates, reducing off-chip memory access by 78%. A pipelined event-driven computation engine and clock-gated spike scheduler minimize dynamic switching, achieving 61% reduction in power and 2.4× throughput improvement compared to conventional CMOS DNN accelerators. The proposed system performs dynamic visual-feature encoding, spike-based temporal fusion, and on-chip learning for anomaly and object detection tasks in low-power sensor nodes. Fabricated in 28-nm CMOS, the prototype achieves 0.29 mW power, 0.42 pJ/spike energy, and 94.3% inference accuracy, outperforming state-of-the-art neuromorphic platforms. Results demonstrate that hybrid spiking architectures integrated with VLSI-efficient plasticity circuits can deliver high-accuracy, self-adaptive AI within stringent edge constraints, enabling next-generation smart-sensing and autonomous micro-robotic intelligence.

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Ravi Shankar P. mail -

S. Balaji mail -

Gokul C. mail -

K. Nagarajan mail -

A. Arulkumar mail -

S. Venkatesh mail

link https://doi.org/10.54216/JISIoT.170228

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2026

A Neutrosophic Framework for Multilevel Corruption Assessment in Central Asian Societies

We introduce a neutrosophic framework to assess corruption across micro, meso, and macro levels and illustrate it with a public, fully synthetic dataset covering five Central Asian societies (2020–2025). The framework models the proposition “High Corruption” with three independent degrees: Truth (T ), Indeterminacy (I), and Falsity (F), which need not sum to one. We propose a summary index—the Neutrosophic Evidence Risk Index (NERI)—that couples evidence for and against high corruption with indeterminacy. Empirically, we document three stylized patterns in the synthetic data: (i) a moderate decline in country-level NERI over time for most countries; (ii) a negative association between region-year e-service adoption and bribe solicitation; and (iii) a negative association between digital government capacity and T at the country-year level. For example, the average bribe-solicitation rate is 0.047 overall, 0.198 without e-services (95% CI 0.175–0.220) vs. 0.019 with e-services (95% CI 0.015–0.022), implying a risk difference of -0.179 and a relative risk of 0.094.

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Samandarboy Sulaymanov mail -

Gafurov Ubaydullo Vakhabovich mail

link https://doi.org/10.54216/IJNS.270238

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

Evaluating Priorities in the Implementation of Microcredentials in Latin American Universities through the Hierarchical Analytical Process for Educational Flexibility

This paper responds to the problem of establishing criteria priority for microcredential implementation in Latin American universities, a developing topic with great momentum and the need to professionalize traditional learning models. Amid rapid digital and labor developments, microcredentials emerge as an efficient way of certifying targeted skills and fostering adaptability to market demand. Still, implementation in higher education lacks a clear pathway of systematic substantiation. The state-of-the-art demonstrates that few mixed-method studies have attempted to prioritize institutional, pedagogical, and technological aspects of this endeavor. This paper applies the Analytic Hierarchy Process (AHP) to a criterion for criteria relative assessment as a method for qualitative and quantitative study. This approach assesses relative importance between seemingly equal criteria—digital infrastructure, teacher training, curriculum relevance, and external validity, for example—for better implementation within higher education systems. Results assess teacher training and platform interoperability as the two most important criteria for successful microcredential implementation. This study is relevant theoretically for multicriteria approaches to the assessment of learning flexibility and practically speaking, supports university administrative decisions for more adaptable, equity-driven and sustainable learning options.

groups

Humberto León Flores mail -

Claudio Ruff Escobar mail -

Natalia Daries Ramón mail

link https://doi.org/10.54216/IJNS.260424

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2025

Modeling Internal Communication in Multicultural Organizations using Neutrosophic Plithogenic Logic

In an increasingly multicultural world, workforces become more diverse, and the challenge of internal communications exacerbates. What one group deems clear communication can easily be reinterpreted, countered or found invalid by another group valuing different cultural mores, norms, and expectations. As these issues grow, not only does organizational coherence suffer, but also strategic impact potential fails as globalized realities emerge. There becomes a need for a model that successfully implements the nuance and indeterminacy of such communicative interactions. While models of intercultural communications exist, they often operate on a binary method of understanding that fails to acknowledge the simultaneous presence of varying levels of truth, indeterminacy, and untruths. This is where neutrosophic plitogenic logic intervenes as the advanced form through which these properties can be modeled to suggest cognitive/emotional/symbolic determination as a single potentialized system of assessment. Thus, the challenge emerges to neutralize indeterminacy by fluidly responding to the communicative elements relative to what is present at any given moment over time. Neutrosophic Plitogenic Logic emerges as a viable interdisciplinary approach to understanding internal communication by theoretical and practical means - using epistemology through organizational studies fields and management feasibility - as it successfully presents the shifting and multivalent form of such a communicative process within increasingly multicultural dynamics when existing reconciled methods fail. This contribution is theoretical - as it creates a tool for fields of study to manage structural ambiguity - and practical - for management purposes - as it fosters a model for inclusion in resilient, contextually viable messaging design.

groups

Karla Melissa Ruiz Quezada mail -

Juan Roberto Pereira Salcedo mail -

Ronald Ricky Alcívar Cabada mail -

Pedro Manuel García Arias mail -

Edison Luis Cruz Navarrete mail

link https://doi.org/10.54216/IJNS.260425

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2025

Modeling Ambiguity in AI-Enhanced Learning: A Neutrosophic Approach to Stance Detection and Causal Evaluation

This work presents a neutrosophic stance detection model to bridge computational assessment and logic of indeterminacy in artificially intelligent (AI)-mediated learning and its outcomes. Utilizing the BART-large-MNLI model, a causal assessment was made of five hypotheses stemming from AI-supported learning between teacher-student relationships. These stances were then transformed into refined neutrosophic values (truth (T), partial support (P_S), indeterminacy (I), partial opposition (P_O) and falsity (F)). Ultimately, findings suggest that partial support is the most prevalent stance applied to any of the hypotheses, revealing that AI is, largely, a boon to education. However, this valence is tempered by indeterminacy among axes as well as stance magnitude. The largest partial support in rank order came from personalized education and access to AI tutors, while the most importance was given to opposition of relying on AI as support and replacement AI learning. Such findings confirm neutrosophic stance analysis and causal graph modeling as increasingly successful for applying measurable patterns to epistemically ambiguous fields. The neutrosophic causal graph integrates the above findings with a visualization of proposed dynamics between each vertex based on both quantitative patterns and epistemic uncertainty trends. The current research holds implications for educational theory, policy and instructional design integrity in 21st century learning. Uncertainty became a tangible concept; instead of devaluing AI in the classroom, it must be present as an enhancing supplemental tool, never replacement, for ethical considerations and equitable access. The potential for neutrosophic to transform apparent truths that are at times contradictory is confirmed through the human-machine interactive learning process, with subsequent suggestions for future research into AI-mediated education's causal relationships and decision-making potential.

groups

Oscar José Alejo Machado mail -

Adriana María Estupiñán Sera mail -

Maikel Y. Leyva Vazquez mail -

Florentin Smarandache mail

link https://doi.org/10.54216/IJNS.260426

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2025

Neutrosophic–Plithogenic IADOV for Capturing Subjective Meaning in Qualitative Research

The research problem of this endeavor was how to comprehend subjective meanings based upon qualitative research—a uniform methodological concern of the social sciences for phenomena perpetually occurring in ever-changing environments. The significance of the study is based upon a methodological necessity that transcends natural human capability to engage what it means to be "uncertain" now that so much social reality has been rendered tainted, in addition to cultural and contextual amalgamation. Nonsensical articles provide one form of understanding but fail to equip uncertainty—acknowledgement of where something can go one way or the other, or multiple—and multivalent perspectives ultimately render ineffective opportunities for researchers to adopt a quantifiable, absolute reality. Therefore, this endeavor applies the Iadov Plithogenic Neutrosophic approach, compounded of plithogenic logic with neutrosophic meaning to successfully navigate qualitative research with high uncertainty. Ultimately, findings support that this approach empowers researchers to remove layers and find meaning in more original and texture-based fashions than the traditional A/B/C option. Ultimately, this work contributes theoretically to qualitative researches with an integrative new approach to render subjectivity, while practically providing those who want to understand a complicated world, the works to be effective. This endeavor illustrates that the most effective flexible approach to render meaning knowledge is the only way to go when the undertaking exists in comprehensive arenas.

groups

Carmen Marina Méndez Cabrit mail -

Josía Jeseff Isea Argüelles mail -

Danny Mauricio Sandoval Malquín mail -

Roberth Alexander Anamá Tiracá mail

link https://doi.org/10.54216/IJNS.260328

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

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