Modeling bladder cancer survival function based on neutrosophic inverse Gompertz distribution

Oday Esam; Zeina Ameer; Zakariya Yahya

doi:https://doi.org/10.54216/IJNS.250106

Journal of Intelligent Systems and Internet of Things | 2024

Advances in Wearable Sensors for Real-Time Internet of things based Biomechanical Analysis in High-Performance Sports

Interest in wearable technology and the need for eco-friendly solutions have spurred new methodologies. This research examines how sophisticated deep learning and biomimetic designs benefit each other. The results may change smart technology forever. The introduction highlights the global appeal of wearable technology and the importance of environmental considerations in design. Deep learning and biomimicry are a fresh and exciting combination that might increase smart device accuracy, energy efficiency, and biomimicry. This project seamlessly integrates biomimetic design elements with deep learning methods. Biomimicry affects wearable technology design and functioning. However, deep learning techniques based on artificial neural networks boost user flexibility and predictive analytics. The controlled experiment allows a thorough examination of a number of datasets designed to cover a wide range of biomimetic settings and user behaviours. The data prove that the proposed technique beats alternatives across several performance parameters. Integrating biomimetic principles with deep learning systems boosts accuracy. This proves the system's reliability. The biomimetic method is eco-friendly since energy efficiency grows dramatically. Biological mimicry indications show that the suggested strategy resembles natural systems. A new exploratory method enhances sustainable technologies. Integrating biomimicry and deep learning efficiently enhances gadget performance and meets environmental standards. This research emphasizes the transformational power of nature-friendly technology, changing our worldview. Our study helps ensure that upcoming wearable technologies are cutting-edge and ecologically beneficial. Deep learning and biomimetic designs are converging, marking a tipping point in sustainable technology. This helps move toward an eco-friendly future.

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Vilas Alagdeve mail -

Ranjan K. Pradhan mail -

R. Manikandan mail -

P. Sivaraman mail -

Sarihaddu Kavitha mail -

Shaeen Kalathil mail

link https://doi.org/10.54216/JISIoT.130209

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

SOM and Hybrid Filtering: Pioneering Next-Gen Movie Recommendations in the Entertainment Industry

In an age where digital connectivity is increasingly shaping entertainment content, personalized movie recommendations play a pivotal role in enhancing user satisfaction and engagement. This research introduces an innovative approach utilizing Enhanced Self-Organizing Maps (SOM) to streamline movie selection processes. Self-Organizing Maps (SOMs), a type of unsupervised neural network architecture, are particularly adept at discerning intricate data patterns, making them valuable assets in recommendation systems. The methodology outlined in this paper commences with gathering user-movie interaction data, including user feedback and movie characteristics, which is standardized to ensure consistency before model training. Leveraging its adaptable learning rate and neighborhood function, the Enhanced SOM effectively identifies subtle data nuances. Personalized movie suggestions are then generated by exploiting the Enhanced SOM's capacity to identify similar users and films. Integration of hybrid filtering techniques enriches recommendation quality, blending collaborative filtering algorithms, which leverage user-item interactions, with content-based filtering, which utilizes movie attributes such as genres and descriptions. This amalgamation results in suggestions that harmoniously combine diverse filtering methodologies. The proposed solution's efficacy is rigorously evaluated by comparing suggestion accuracy and user satisfaction against predefined benchmarks. Extensive real-world dataset testing corroborates the effectiveness of the Enhanced SOM-based movie recommendation approach. Furthermore, the system offers flexibility through options for parameter adjustment, grid size variations, and neighborhood function modifications to further refine recommendation accuracy. Collectively, these elements underscore the efficacy of the proposed method in furnishing tailored movie recommendations. When coupled with hybrid filtering techniques, the implementation of Enhanced SOMs emerges as a reliable model for content platforms seeking to enhance user experiences by delivering precise movie recommendations, coupled with scalability and adaptability.

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Saurabh Sharma mail -

Ghanshyam Prasad Dubey mail -

Harish Kumar Shakya mail -

Aditi Sharma mail

link https://doi.org/10.54216/FPA.160204

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Employing Deep Learning Techniques for the Identification and Assessment of Skin Cancer

These days, skin cancer is a prominent cause of death for people. Skin cancer is the name given to the abnormal development of skin cells that are exposed to the sun. These skin cells can develop anywhere on the human body. The majority of malignancies are treatable in the early stages. Thus, early detection of skin cancer is anticipated in order to preserve patient life. With cutting edge innovation, it is possible to detect skin cancer early on. Here, we provide a novel framework for the recognition of dermo duplication pictures that makes use of a neighbouring descriptor encoding method and deep learning technique. Specifically, the deep representations of a rescaled dermo duplication image that were initially removed through training an extraordinarily deep residual neural network on a big dataset of normal images. Subsequently, the neighbourhood deep descriptors are obtained by request-less visual measurement highlights, which rely on fisher vector encoding to create an international image representation. Lastly, a convolution neural network (CNN) was utilised to orchestrate melanoma images employing the Fisher vector encoded depictions. This proposed technique can give more discriminative parts to oversee huge contrasts inside melanoma classes and little varieties among melanoma and non-melanoma classes with least readiness information.

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Sowmya Koneru mail -

Pappula Madhavi mail -

Krishna Kishore Thota mail -

Janjhyam V. Naga Ramesh mail -

Venkata Nagaraju Thatha mail -

S. Phani Praveen mail

link https://doi.org/10.54216/FPA.160205

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Cybersecurity and Information Management | 2024

A Novel Secured Deep Learning Model for COVID Detection Using Chest X-Rays

Automatic detection of a medical disease is a need of the hour as it helps doctors diagnose diseases and provide fast medical reports. COVID-19 is a deadly disease for which an automated detection system may be helpful. This study proposes a unique hybrid deep learning model, COVIDet, based on CNN and the speeded-up robust features (SURF) extraction approach to diagnose COVID-19 using chest x-ray images. SURF is utilized in this work to extract features, and the output is then transferred to a 25-layer CNN for detection using the extracted features. This investigation employed 4623 COVID-19 positive X-ray pictures or 8055 total. The suggested hybrid model also contrasts with the study's VGG19, Resnet50, Inception, Xception, and traditional CNN models. The proposed model had a 98.01% accuracy, a 97.03% F1-score, a 98.65% sensitivity, a 99% precision, and a 95.65% specificity. The proposed model can be further improved when more datasets are available and can help doctors to diagnose patients quickly and efficiently. Using chest X-ray pictures, a secured web application is also developed to identify COVID-19. The user sends the application a chest X-ray image, and in return, it determines whether an individual is COVID-19 positive or not, cutting down on testing time. In Covid times, when people are standing in long queues and waiting for their turns, this application would greatly help. The application uses the pre-trained COVIDet model in the backend.

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Chhaya Gupta mail -

Vasima Khan mail -

Ramya Srikanteswara mail -

Nasib Singh Gill mail -

Preeti Gulia mail -

Sindhu Menon mail

link https://doi.org/10.54216/JCIM.140116

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

An Integrated DEMATEL with Bipolar neutrsophic Dombi-based Heronian Mean Operator and Its Applications in Decision-making Problem

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is commonly used in examining and illustrating the relationship between different factors in a complex system. This paper proposes a novel approach that integrates the Bipolar neutrosophic Dombi-based IGWHM operator into the DEMATEL method, in which the criteria are analyzed by means of the cause-and-effect relationship diagram. The current studies on the classical DEMATEL approach have some limitations on the aggregation process, particularly in capturing the interrelationship of individual arguments by assessing their impact on each other within a complex system. To enhance the aggregation of complex information in the decision-making framework, the Bipolar neutrosophic Dombi-based Improved Generalized Weighted Heronian mean (IGWHM) operators are employed. The applicability and effectiveness of the proposed approach are demonstrated when solving a selection of transport service providers. The ability of the method to highlight the intricate interdependencies and ranking criteria based on their importance. The sensitivity of the developed approach is observed with variations in the involved parameter. Moreover, a comparative analysis is made with other methods to demonstrate its validity.  

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Siti Nurhidayah Yaacob mail -

Hazwani Hashim mail -

Nor Hashimah Sulaiman mail -

Noor Azzah Awang mail -

Ashraf Al-Quran mail -

Lazim Abdullah mail

link https://doi.org/10.54216/IJNS.250101

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

On Some Topological Spaces Based On Symbolic n-Plithogenic Intervals

In this paper, we present the topological space of intervals based symbolic m-plithogenic real numbers of orders between 2 and 5, where we clarify how m-plithogenic real intervals can be expressed according to the symbolic plithogenic partial order relation, and we use these intervals to build a topological space. On the other hand, many illustrated and related examples on open and closed sets will be provided to explain the validity of our approach.    

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Raed Hatamleh mail -

Ayman Hazaymeh mail

link https://doi.org/10.54216/IJNS.250102

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

Enhancing Network Security using Possibility Neutrosophic Hypersoft Set for Cyberattack Detection

Network security is any endeavor intended to defend the integrity and usability of the data and network. Fast development in network technology and the scope and amount of information transported on a network is gradually growing. Based on these situations, the complexity and density of cyber-attacks and threats are also increasing. The constantly expanding connectivity makes it more difficult for cyber-security specialists to monitor all the movements on the network. More complex and frequent cyber-attack makes anomaly identification and detection in network events challenging. Machine learning (ML) provides different techniques and tools to automate cyber-attack detection and for prompt prognosis and analysis of attack types. The model of a neutrosophic hypersoft set (NHSS) is a combination of a neutrosophic set with a hypersoft set. It is a useful structure to handle multi-objective problems and multi-attributes with disjoint attributable values. This study derives the Possibility Neutrosophic Hypersoft Set for Cyberattack Detection (pNHSS-CAD) technique to improve network security. The pNHSS-CAD method has its formation in feature selection with the Whale Optimization Algorithm (WOA), which successfully recognizes the important features from the data, thus improving processing speed and reducing dimensionality. Following feature selection, the pNHs-set classifier is employed for the robust detection and identification of cyber-attacks, which leverages the power of the neutrosophic set to deal with ambiguity and uncertainty in the information. The Firefly (FF) technique is applied for hyperparameter fine-tuning, which ensures the model operates at maximum effectiveness to enhance the performance of the classification. This wide-ranging method leads to a very efficient cyberattack recognition method, which can able to accurately mitigate and identify risks in the real world

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Mohammed Abdullah Al-Hagery mail -

Abdalla I. Abdalla Musa mail

link https://doi.org/10.54216/IJNS.250103

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

Integrating Neutrosophic Vague N-Soft Sets with Chimp Optimization Algorithm for Sentiment Analysis on Social Media

The swift development in social media through the internet produces vast data in a real-time scenario that has startling effects on large datasets. It generated the high-level use of sentiments and emotions in social networking media. Sentiment analysis (SA) using a neutrosophic set presents a new technique to handle the integral ambiguity and uncertainty in text datasets. Different from classical approaches, which categorize sentiment as positive, negative, or neutral, the neutrosophic set allows for the comparison analysis of truth-, indeterminacy-, and falsie-membership functions for all the sentiments. This allows a more flexible and nuanced representation of sentiments, which accommodates the contradictions and complexities commonly depicted in natural language. SA can accomplish high performance and depth in interpreting and understanding the emotions expressed in uncertain and diverse text datasets by leveraging a neutrosophic set. This manuscript presents a Neutrosophic Vague N-Soft set with a Chimp Optimization Algorithm for Sentiment Analysis (NVNSS-COASA) technique on Social Media. The NVNSS-COASA technique is initiated by the comprehensive preprocessing stage to normalize and clean the text dataset, which ensures superior input for the succeeding stage. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) mechanism is employed to convert the preprocessed text into mathematical features, which capture the word importance in terms of datasets. Subsequently, a strong NVNSS classifier is employed for accurately categorizing the sentiment. We integrate COA for the parameter tuning to further improve the performance of the method. The simulation outcomes emphasized that the NVNSS-COASA method shows superior outcomes over other techniques. The outcomes indicated that the NVNSS-COASA can able to deliver reliable and precise insights from the text dataset.

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Imène Issaoui mail -

Afef Selmi mail

link https://doi.org/10.54216/IJNS.250104

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Modelling of Neutrosophic Set-Based k-Nearest Neighbors Classifier for Virus Pneumonia and COVID-19 Recognition

COVID19 otherwise called Severe Acute Respiratory Syndrome Corona virus-2 is an infectious illness. Another transmittable infection called Pneumonia is mainly attributable to infection because of bacteria in the alveoli of the lungs. Once a diseased lung tissue has infection, it elevates excretion in it. Specialists conduct health examinations and identify the patient through ultrasound, biopsy, or Chest X-ray of lungs to identify whether the patient has these diseases. Incorrect treatment, misdiagnosis, and if the disease was disregarded will result in the fatality. The development of Deep Learning and neutrosophic set (NS) supports the decision-making procedure of professionals to identify patients with this disease. NS is a prolongation of the fuzzy set and classical theories. The NS determines three memberships such as T, I and F. T, I, and F display the degree of truth, the false, and the indeterminacy membership, correspondingly. This enables a more nuanced representation of contradiction, uncertainty, and ambiguity within the dataset, allowing superior handling of imprecise and complex data. This study develops a new Deep learning with Neutrosophic Set-Based k-Nearest Neighbors Classifier for disease detection (DLNSKNN-DD) technique. The major purpose of the DLNSKNN-DD method is to identify the existence of virus pneumonia and COVID-19. In the DLNSKNN-DD technique, the feature extraction from the medical images is carried out by residual network (ResNet50v2). Moreover, the parameter tuning of the ResNetv2 model is done using Adadelta optimizer. The DLNSKNN-DD technique exploits NSKNN model for classification purposes. The performance evaluation of the DLNSKNN-DD algorithm can be assessed on medicinal image dataset. The experimental outcomes underlined the effectual recognition results of the DLNSKNN-DD technique on the identification of diseases  

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Imène Issaoui mail -

Afef Selmi mail

link https://doi.org/10.54216/IJNS.250105

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Modeling bladder cancer survival function based on neutrosophic inverse Gompertz distribution

In the field of survival analysis, the inverse Gompertz distribution is used to mimic human lifetime data patterns. The goal of the neutrosophic inverse Gompertz distribution (NIGD) is to describe a range of indeterminate survival data. The defined distribution is very helpful for modeling somewhat positively skewed unknown data. The main statistical characteristics of the created NIGD, such as the neutrosophic moments, hazard rate, and survival function, are covered in this paper. Additionally, the well-known maximum likelihood estimation method is used to estimate the neutrosophic parameters. A simulation study is conducted to see whether the projected neutrosophic parameters were reached. Not to mention that possible real-world uses of NIGD have been discussed using actual data. To show how well the suggested model performed in comparison to the present distributions, real data were used.  

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Oday Esam Al-Saqal mail -

Zeina Ameer Hadied mail -

Zakariya Yahya Algamal mail

link https://doi.org/10.54216/IJNS.250106

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new