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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

Details open_in_new
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
Pure Mathematics for Theoretical Computer Science | 2024

Some Results About αc–Continuous Functions

The main aim of this paper is to study new class of continuous function is called  – continuous function. for this aim, the nation of - open and pre – open sets and  - compact space are introduced. and we shall study the relationship between  – continuous and pc– continuous functions.

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Ahmed Rida Fares mail
link https://doi.org/10.54216/PMTCS.030103

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new
International Journal of Neutrosophic Science | 2024

RETRACTED ARTICLE: On the Characterization of Some m-Plithogenic Vector Spaces and Their AH-Substructures Under the Condition 6 ≤ dim SPV ≤10

This paper is concerned with studying symbolic m-plithogenic vector spaces with finite orders between 6 and 10, where it defines and characterizes the AH-subspaces, AH-kernels, and AH- linear transformations in five different symbolic m-plithogenic spaces (6-plithogenic, 7-plithogenic,10-plithogenic vector spaces). Also, we prove many theorems that describe the computation of the kernels and direct images of the plithogenic AH-linear transformations.

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Ibraheem Abu Falahah mail -
Abdallah Al-Husban mail -
Mutaz M. Abbas Ali mail -
Ahmed A. Alsaraireh mail -
Amani Shatarah mail -
Norah Mousa Alrayes mail
link https://doi.org/10.54216/IJNS.240322

Volume & Issue

Vol. Volume 24 / Iss. Issue 3

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Journal of Artificial Intelligence and Metaheuristics | 2024

Advances and Challenges in Feature Selection Methods: A Comprehensive Review

The feature selection area in data analytics is explored through a comprehensive literature review, and the increasing areas that have a data dependency problem and are being resolved with feature selection are highlighted. Review topics of this course cover the foundations to present use cases, for example, cybersecurity, healthcare, and finance. Particularly crucially for the healthcare domain, it reduces the dimensionality and elucidates complex causal links. The further investigation overlaps contemporary techniques, including optimization-based methods, swarm intelligence and algorithms for the diagnosis of heart diseases. The conclusion builds on the practical assessment and underlines research gaps, serving as a basis to set a diversified technological review. This also exhibits new techniques that have released their efficiency in classification environments, for example, hybrid Ant Colony Optimization and the Gray Wolf Optimizer. The ISSA algorithm stands out as a swarm intelligence technique that is best among others. The paper concludes by demonstrating that feature selection goes beyond the preprocessing stage, but it instead stands as a vital part of the fields of machine learning and data science and thus aids the researchers in both retrospective analysis and forthcoming projects.

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Mohamed Ziad Ali mail -
Abdulrahman Abdullah mail -
Ahmed Mohamed Zaki mail -
Faris H. Rizk mail -
Marwa M. Eid mail -
Elsayed M. El-Kenway mail
link https://doi.org/10.54216/JAIM.070105

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

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Journal of Artificial Intelligence and Metaheuristics | 2024

Exploring Optimization Algorithms: A Review of Methods and Applications

This article review focuses on feature selection as the main parameter that plays a major role in tuning machine learning models. Several algorithms of optimization such as MFO (Moth-Flame Optimization), the GA-GSA algorithm’s hybrid type, SOA (Seagull Optimization Algorithm), WOA (Whale Optimization Algorithm), GOA (Grasshopper Optimization Algorithm), HGSO (Henry Gas Solubility Optimization), and SafeOpt are widely used in engineering design, power systems scheduling, The paper stresses the importance of optimization in improving efficiency, lessening mistakes and increasing understandability of machine learning models. The literature addresses the widest directions in the usage of optimization for the following fields of science such as structural engineering, additive manufacturing, and landslide susceptibility mapping. A comprehensive summary table is generated, which shows an overview of each study, algorithm, focus, and methodology and has a stoke of key findings. The conclusions reveal the adaptiveness, competitiveness and compossibility of the optimization algorithms applied to a wide range of domains. The summary shows how optimization has the potential to change decision-making processes and activities by being a decisive factor that determines the future of branches of various industries. The main objective of this work is to direct researchers and practitioners by providing them with some innovative ideas and approaches and offering insights on the existing cutting-edge approaches while laying the groundwork for future innovations in optimization.

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Abdulrahman Abdullah Farag mail -
Ziad Mohammed Ali mail -
Ahmed Mohamed Zaki mail -
Faris H.Rizk mail -
Marwa M. Eid mail -
El-Sayed M. El-Kenawy mail
link https://doi.org/10.54216/JAIM.070201

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new