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

Abdulsalam; Amirah; Yaseen S.

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

Journal of Cognitive Human-Computer Interaction | 2025

Proposed Strategies for Sustainable Agriculture Domain Chatbot with Blockchain Development

The farm sector is challenged by various factors, such as climate volatility, ineffective resource management, and data security. In this paper, a new methodology is proposed where blockchain technology is combined with a chatbot platform to offer farmers real-time, secure, and accurate crop suggestions. Blockchain allows data integrity to be guaranteed, reducing risks from data tampering. The chatbot is an interactive platform, where farmers can enter soil parameters, location, and weather. The system processes these inputs and gives optimal crop recommendations based on past data and predictive analytics. The proposed solution is enhancing sustainable agriculture practices, boosting productivity, and ensuring long-term food security.

groups

S. Rahini Sudha mail -

Uma mageshwari D. mail -

Vaishnavi N. G. mail -

Sumetha S. S. mail -

Subbalakshmi B. V. mail -

Poovizhi A. mail

link https://doi.org/10.54216/JCHCI.090203

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Review of Machine Learning Technique based Prediction Model for Phishing Websites Detection

Phishing attacks have emerged as a significant cybersecurity challenge, targeting individuals and organizations by tricking users into revealing sensitive information through deceptive websites. Traditional phishing detection methods, such as blacklists and heuristic-based approaches, struggle to keep pace with the rapid evolution of phishing techniques. Machine learning-based predictive models offer a promising solution by analyzing website attributes, URL structures, and behavioral patterns to distinguish between legitimate and phishing websites. This paper provides a comprehensive review of various machine learning techniques, including decision trees, support vector machines (SVM), random forests, deep learning models, and ensemble methods, employed in phishing website detection. It explores feature selection strategies, dataset characteristics, performance evaluation metrics, and real-world implementation challenges. Furthermore, the study discusses recent advancements such as adversarial resilience, natural language processing (NLP) integration, and real-time phishing detection frameworks. The review highlights existing research gaps and future directions to enhance phishing detection accuracy, scalability, and adaptability in evolving cybersecurity landscapes.

groups

RishiKesh Dube mail -

Twinkle Sharma mail -

Damodar Tiwari mail -

Kailash Patidar mail

link https://doi.org/10.54216/JCHCI.090204

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Journal of Cognitive Human-Computer Interaction | 2025

Injury prediction and prevention for cricket players using AI

Cricket is a physically demanding sport that exposes players to various acute and chronic injuries. Preventing these injuries is crucial for maintaining peak performance and prolonging careers. This project leverages artificial intelligence (AI) and machine learning (ML) to analyze key player data, including biomechanics, workload, fatigue, and mental stress, to assess and mitigate injury risks. Wearable sensors and tracking systems continuously monitor player movements, workload, and physiological parameters, providing real-time insights into their physical condition. By detecting patterns that indicate potential injury risks, the AI model enables early intervention through personalized training modifications and recovery programs. This proactive approach minimizes injuries, optimizes player fitness, and enhances performance. Ultimately, integrating AI-driven injury prevention strategies in cricket ensures better player management, increased longevity, and improved overall team efficiency.

groups

A. V. Adlin Grace mail -

Sanjay Kumar S. mail -

Rajesh S. mail -

Ragul Doss R. mail

link https://doi.org/10.54216/JCHCI.090205

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Hybrid Deep Learning Models for Finger Vein Biometric Authentication with Experimental Insights and Robust Performance Evaluation

The proposed method creates an advanced Deep Residual Convolutional Neural Network (DR-CNN) for finger vein pattern recognition to enhance both accuracy and computational efficiency of the system. The framework implements DR-CNN to handle the reduction of dimensions together with feature extraction while resolving traditional CNN models' overfitting issues. This research utilizes 6,000 images from the VERA and PLUSVein FV3 and MMCBNU_6000 and UTFV databases which form 80% training data and 20% testing data. The ImageNet training includes 4 pooling layers while also using 4 fully connected layers as well as 13 convolutional layers. The DR-CNN classifier achieves optimal authentication-performance through its implementation of Gray Level Co-occurrence Matrices (GLCM) and Scale-Invariant Feature Transform (SIFT) for extracting features. A performance assessment based on accuracy, sensitivity, specificity, F1-score, false acceptance rate (FAR) and false rejection rate (FRR) proves that DR-CNN surpasses traditional techniques. With its implementation of 5,000 images the proposed model demonstrates better accuracy (94.39%) than CNN (92.45%), RNN (88.99%) and DNN (85.91%). Tests show that the system processes 25,000 images within 2.43 milliseconds establishing fast computation speeds. DR-CNN achieves robustness through minimum mean absolute error values of 19.34. The proposed DR-CNN model delivers a 97.8% recognition rate together with a 0.83% error rate which proves its effectiveness for biometric security applications.

groups

Hashem Alyami mail

link https://doi.org/10.54216/FPA.200108

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

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

An Efficient Detection of Copy-Move Forgery Using Phase Correlation

Creating images is one of the focuses of digital image processing. There are multiple techniques to spot image fraud. This work proposes a new approach to detect attacks that mimic Copy-Move forgeries. The proposed method applies DWT on the input image to create a reduced dimensional representation of the image. After that, the compressed image is divided into overlapping blocks. After these blocks are sorted, phase correlation is utilized as a similarity criterion to find duplicate blocks. Due to DWT usage, the lowest-level picture representation is first employed for detection. This work also covers the examination of numerous limits that are imposed to the input image, and the results are used in the analysis that follows.

groups

L. Chitirap Paavai mail -

V. Vadivu mail -

L. Krishnan 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.

groups

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.

groups

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

Details open_in_new

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.

groups

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.

groups

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