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International Journal of Neutrosophic Science | 2024

A New Version of Gumbel Distribution Using Sine Technique Family: Properties, Parameter Estimation, and Data Analysis and Comparison with Fuzzy Data

In this paper, we propose a new version of the Gumbel Distribution using a sine technique family. We discuss the key properties of this distribution, such as the probability density function, the cumulative distribution function, the survival function, the hazard function, the cumulative hazard, and the moments. Additionally, we present a method for estimating the distribution's parameters. We then analyze a dataset using the original and generalized distributions, comparing the results and using goodness-of-fit measures to determine which distribution best fits the data. Finally, we provide conclusions based on our findings, with many examples and valid comparisons applied on fuzzy data.

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Hanaa Saad M. Shibeeb mail -
S. Altalaqani mail -
A. AL-Adilee mail
link https://doi.org/10.54216/IJNS.230327

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

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International Journal of Neutrosophic Science | 2024

Neutrosophic Meta SHAP and Neutrosophic Meta LIME: An Efficient Framework for Explainable AI in Oral Cancer Detection

Among the current generation researcher, artificial intelligence has played vital role in various fields, including healthcare. One of the key areas where it has shown enormous potential is in cancer detection and treatment. AI and methods of machine learning algorithms have been applied to analyze large datasets, such as genomics, transcriptomic, and imaging data, to identify patterns and relationships that can help in cancer diagnosis and therapy. However, due to the inherent complexity and heterogeneity of tumors in individual patients, building a diagnostic and therapeutic platform that can accurately analyze outputs becomes a challenging task. To address this challenge, researchers have proposed the use of explainable AI frameworks in cancer detection. Explainable AI frameworks aim to provide transparency and comprehensibility to the decision-making process of AI algorithms, ensuring that the predictions or classifications generated by these algorithms can be understood and trusted by healthcare professionals. One popular explainable AI method is SHAP (SHapley Additive explanations). SHAP is a well-known XAI method that provides intuitive and interpretable feature importance [13] for individual predictions. Another explainable AI method is LIME (Local Interpretable Model-agnostic Explanations), which generates posthoc explanations and is suitable for quick and satisfactory explanations. These existing explainable AI methods, however, have limitations in their applicability to cancer detection. Therefore, in this research article, we propose the use of two novel frameworks: Neutrosophic Meta SHAP and Neutrosophic Meta Lime. Neutrosophic Meta SHAP and Neutrosophic Meta Lime are efficient frameworks specifically designed for the analysis and interpretation of AI models in oral cancer detection.

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Sakshi Taaresh Khanna mail -
Sunil Kumar Khatri mail -
Neeraj Kumar Sharma mail
link https://doi.org/10.54216/IJNS.230328

Volume & Issue

Vol. Volume 23 / Iss. Issue 3

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International Journal of Artificial Intelligence and Education Technology |

Synergistic Navigation Control for Mobile Robots: Integrating Type-2 Fuzzy Logic and Neural Networks.

Intelligent mobile robots operate in environments characterized by various uncertainties, necessitating effective navigation strategies to accomplish tasks such as path tracking and obstacle avoidance. This research employs a omni drive mobile robot to autonomously reach predefined targets in diverse scenarios within static and dynamic environments. The study evaluates two distinct controllers, a fuzzy logic controller and a neural network controller, employed to guide the mobile robot safely towards its destination while mitigating collision risks with obstacles. These controllers regulate the mobile robot linear and angular velocities, ensuring adaptive navigation in real-time. Experimental results underscore the efficacy and adaptability of each controller, particularly in addressing uncertainty challenges inherent in mobile robot navigation. Through systematic evaluation and comparison, insights are gained into the relative performance and suitability of fuzzy logic and neural network controllers in enhancing mobile robot autonomy and robustness. This research contributes to advancing the understanding of navigation techniques in mobile robotics, facilitating the development of more efficient and reliable autonomous systems for real-world applications.

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International Journal of Artificial Intelligence and Education Technology |

Synergistic Navigation Control for Mobile Robots: Integrating Type-2 Fuzzy Logic and Neural Networks.

Intelligent mobile robots operate in environments characterized by various uncertainties, necessitating effective navigation strategies to accomplish tasks such as path tracking and obstacle avoidance. This research employs a omni drive mobile robot to autonomously reach predefined targets in diverse scenarios within static and dynamic environments. The study evaluates two distinct controllers, a fuzzy logic controller and a neural network controller, employed to guide the mobile robot safely towards its destination while mitigating collision risks with obstacles. These controllers regulate the mobile robot linear and angular velocities, ensuring adaptive navigation in real-time. Experimental results underscore the efficacy and adaptability of each controller, particularly in addressing uncertainty challenges inherent in mobile robot navigation. Through systematic evaluation and comparison, insights are gained into the relative performance and suitability of fuzzy logic and neural network controllers in enhancing mobile robot autonomy and robustness. This research contributes to advancing the understanding of navigation techniques in mobile robotics, facilitating the development of more efficient and reliable autonomous systems for real-world applications.

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Fusion: Practice and Applications |

Synergistic Navigation Control for Mobile Robots: Integrating Type-2 Fuzzy Logic and Neural Networks.

Intelligent mobile robots operate in environments characterized by various uncertainties, necessitating effective navigation strategies to accomplish tasks such as path tracking and obstacle avoidance. This research employs a omni drive mobile robot to autonomously reach predefined targets in diverse scenarios within static and dynamic environments. The study evaluates two distinct controllers, a fuzzy logic controller and a neural network controller, employed to guide the mobile robot safely towards its destination while mitigating collision risks with obstacles. These controllers regulate the mobile robot linear and angular velocities, ensuring adaptive navigation in real-time. Experimental results underscore the efficacy and adaptability of each controller, particularly in addressing uncertainty challenges inherent in mobile robot navigation. Through systematic evaluation and comparison, insights are gained into the relative performance and suitability of fuzzy logic and neural network controllers in enhancing mobile robot autonomy and robustness. This research contributes to advancing the understanding of navigation techniques in mobile robotics, facilitating the development of more efficient and reliable autonomous systems for real-world applications.

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Journal of Intelligent Systems and Internet of Things | 2024

Automated EEG based Emotion Detection using Bonobo Optimizer with Deep Learning on Human Computer Interaction

Recently, Emotion detection utilizing EEG signals develops popularity in domain of Human-Computer Interaction (HCI). EEG (electroencephalography) is a non-invasive approach, which processes electrical action from the brain through electrodes located in the scalp. An emotion recognition approach could not only be significant for healthy people among them disabled persons for detecting emotional changes and is utilized for different applications. It is significant to realize that emotion recognition in EEG indications is a difficult task owing to difficult and subjective nature of emotions. In recent times, Machine learning (ML) algorithms like Random Forests or Support Vector Machines (SVM) and Deep Learning (DL) systems namely Recurrent Neural Network (RNN) or Convolutional Neural Network (CNN) are trained on EEG feature extracted and connected emotional labels for classifying the user emotional state. This study presents an Automated EEG-based Emotion Detection using Bonobo Optimizer with Deep Learning (AEEGED-BODL) technique on HCI applications. The goal of the study is to analyze the EEG signals for the classification of several kinds of emotions in HCI applications. To achieve this, the AEEGED-BODL technique uses Higuchi fractal dimension (HFD) approach for extracting features in the EEG signals. Besides, the AEEGED-BODL technique makes use of the quasi-recurrent neural network (QRNN) approach for the detection and classification of distinct kinds of emotions. Furthermore, the BO system was demoralized for optimum hyperparameter selection of QRNN model, which helps in attaining an improved detection rate. The simulation validation of AEEGED-BODL algorithm was simulated on EEG signal database. The comprehensive result stated best outcome of the AEEGED-BODL algorithm over other recent approaches

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Siva Satya Sreedhar P. mail -
M. S. Minu mail -
P. Vidyasri mail -
Habeeb Omotunde mail -
A. Tamizharasi mail -
R. Logarasu mail -
Rama Prabha K. P. mail -
V. Subashree mail
link https://doi.org/10.54216/JISIoT.120106

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2024

Multimodal Feature Fusion Using Optimal Transfer Learning Approach for Lung Cancer Detection and Classification on CT Images

Lung cancer detection is the process of detecting the presence of lung tumor or abnormalities in the lungs. Early diagnosis is crucial for increasing the chances of patient survival and successful treatment. When compared to X-rays, Computed Tomography (CT) images are more sensitive and are increasingly being used for the diagnosis and screening of lung tumors. They provide complete cross-sectional images of the lungs and it will even detect small lesions. AI and Machine learning (ML) approaches are most commonly employed to analyse medical images (e.g. CT scans) and detect lung cancer. This algorithm can help radiologists identify patterns indicative or subtle abnormalities of cancer. Medical diagnosis, particularly in complex diseases such as lung cancer, frequently involves ambiguity. The diagnostic system can alleviate ambiguity via cross-verifying findings from various sources by fusing multimodal features. Multimodal feature fusion using deep learning (DL) algorithm is an advanced technology that leverages the abilities of deep neural networks to combine data from three different modalities or sources for better robustness in several applications, namely natural language processing, image, and data analysis, etc. This study introduces a Multimodal Feature Fusion using an Optimal Transfer Learning Method for Lung Cancer Detection and Classification (MFFOTL-LCDC) methodology on CT images. The chief objective of the MFFOTL-LCDC methodology is to exploit the feature fusion process for the identification and classification of lung tumor. To attain this, the MFFOTL-LCDC model undergoes a multimodal feature fusion approach to derive feature vectors using 3 DL approaches such as SqueezeNet, CapsNet, and Inception v3 models. Besides, the MFFOTL-LCDC technique applies the remora optimization algorithm (ROA) for the hyperparameter choice of 3 DL models. For lung cancer recognition, the MFFOTL-LCDC algorithm exploits the deep extreme learning machine (DELM) algorithm. A series of simulations were conducted to ensure the greater lung cancer recognition outcomes of the MFFOTL-LCDC methodology. The extensive outcomes determine the improved results of the MFFOTL-LCDC technique over recent DL approaches.

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B. Karthikeyan mail -
N. Seethalakshmi mail -
V. Nandhini mail -
D. Vinoth mail -
P. Muthusamy mail -
Kiran Bellam mail
link https://doi.org/10.54216/JISIoT.120107

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2024

Spider Monkey Optimization with Deep Learning-based Hindi Short Text Sentiment Analysis

Sentiment analysis (SA) intends to categorize a text respective to sentimental polarity of individual opinions, like neutral, positive, or negative. The study of Hindi is limited because of the grammatical and morphological complexities of the Hindi language while many research work concentrates on drawing features from English text. The hindi languages make the sentiment classification procedure for Hindi short text a tedious process. The Hindi language has complicated morphology and variation based on phonetics, spelling, and vocabulary; the common usage of numerous dialects between Hindi in India produces a massive volume of glossaries. In this study, we introduce a Spider Monkey Optimization with stacked recurrent neural network (SMO-SRNN) for short text SA on Hindi Corpus. The proposed SMO-SRNN technique mainly aims to identify and categorize the Hindi short text into three distinct classes, namely negative, positive, and neutral. In the presented SMO-SRNN method, the SRNN approach is exploited for the investigation and classification of sentiment. Moreover, the SMO model is employed to finetune the hyperparameter related to the SRNN model. A detailed set of experiments is applied to ensure the high efficiency of the SMO-SRNN algorithm. The comparative outcome highlighted the enhancement of the SMO-SRNN technique over other methods. 

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Praloy Biswas mail -
A. Daniel mail -
Subhrendu Guha Neogi mail
link https://doi.org/10.54216/JISIoT.120108

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new