Integrating Coot Optimization Algorithm with Deep Learning based Medical Image Analysis for Pancreatic Cancer Diagnosis

Eiman Talal

doi:https://doi.org/10.54216/FPA.190119

International Journal of Neutrosophic Science | 2025

The Topology T (PR⋆) ^⊛ in the Frame of Primal Topological Spaces

In this paper, we will use the family of regular⁺-open subsets to present and examine two new operators (.){PR⁺}⊛ and Cl{PR⁺}⊛. We demonstrate that, in contrast to the operator (.){PR⁺}⊛, the operator Cl{PR⁺}⊛ is a Kuratowski closure operator. We show that each of these operators lies between two previously defined operators where for each subset H&sube;S, H_Pᶲ&sube;H_{PR⁺}⊛&sube;H_PRᶲ and H&sube;Cl_Pᶲ(H)&sube;Cl_{PR⁺}⊛(H)&sube;Cl_{PR}ᶲ(H). Furthermore, we show that the topology, denoted by T_{PR⁺}⊛, which is obtained by Cl_{PR⁺}⊛ is independent from T and it is finer than T_η⁺, where T_η⁺ is the family of all unions of regular⁺-open subsets of (S, T). Then we demonstrate several fundamental results concerning this new structure and present many illustrative examples that relate to our conclusions. Finally, by using the operator Cl_{PR⁺}⊛ we introduce a new notion namely, P-generalized closed sets, and study some of their basic properties.

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Amani Rawshdeh mail -

Ahmad Al-Omari mail

link https://doi.org/10.54216/IJNS.260127

Volume & Issue

Vol. Volume 26 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

Logarithmic neutrosophic logical communicated to basic interaction aggregating operators using various finite weighted with extension

 In this paper, we present novel techniques for the logarithmic neutrosophic interaction (LogNI) aggregating operator. The new averaging and geometric operations of LogNI numbers are studied using the universal aggregation function. The LogNI are satisfied some algebraic properties. Four novel aggregating operators are presented: LogNI weighted averaging, LogNI weighted geometric, generalized LogNI weighted averaging, and generalized LogNI weighted geometric.

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Nasreen kausar mail -

Swarnakar Dornala mail -

M. S. Malchijah Raj mail -

Ebru Ozbilge mail -

Emre Ozbilge mail

link https://doi.org/10.54216/IJNS.260132

Volume & Issue

Vol. Volume 26 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

ƝҪͳ- Confused Neutrosophic Crisp Sets

The importance of Neutrosophic crisp triple sets and their important effects on our daily lives was and still is aturing point in the history of science, especially mathematical sciences. From here, we began a ƝҪͳ–confused, concept that is based on both ƝҪͳ–interior, and ƝҪͳ– exterior, and important characteristic emerged because of mixing the characteristic of ƝҪͳ– interior and ƝҪͳ– exterior sets. We supported this with various examples.

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Asra Mohammed Jasem mail -

L. A. A. Al-Swidi mail -

Ali H. M. Al-Obaidi mail

link https://doi.org/10.54216/IJNS.260133

Volume & Issue

Vol. Volume 26 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science |

Beta Special Function of Symbolic 2-Plithogenic and 3-Plithogenic Real Numbers

The main goal of this paper is to define and study the concept of beta special function defined over the ring of symbolic 2-plithogenic numbers and symbolic 3-plithogenic numbers. Besides, we prove some of the elementary properties of these two versions of beta function by using the isomorphism that connect plithogenic numbers with the classical real numbers. In addition, we represent the relationships between plithogenic beta functions and classical beta functions using the same proposed technique.

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Nabil Khuder Salman mail

link https://doi.org/10.54216/IJNS.260134

Volume & Issue

Vol. Volume 26 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

A Robust Disease Prediction System Using Hybrid Deep Neural Networks

One of the most intriguing study subjects in the scientific world is medical data visualization. Researchers focus more on creating a medical that is reliable and efficient. Over the past ten years, varieties of methods have been developed, and investigation is still ongoing to improve healthcare systems' efficiency. To forecast or identify illnesses from medical information, the first stage in medical evaluation of information systems uses statistical techniques. However, statistical techniques yield unreliable findings due to the high amount and variety of the data, which affects the performance of the healthcare system. Numerous methods and solutions for conventional problems were made possible by the advancement of technology and the implementation of AI in the clinical field. To improve patient results and save medical expenses, acute illness prediction is essential. With an emphasis on diabetes, CVD, and specific cancers, this study investigates the effectiveness of many hybrid DL approaches in forecasting the beginning of chronic illnesses. Using a varied dataset of 100 thousand patient records, we evaluated the performance of a few hybrid methods, such as Autoencoder-Support Vector Machine (AE-SVM), Gradient Boosting-Neural Network (GB-NN), and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM). Our findings show that when it came to forecasting the development of disease within a period of five years the CNN-LSTM model offered the greatest accuracy of 95.3%, closely followed by GB-NN with 94.1% and AE-SVM with 92.8%. Along with discussing the possible incorporation of these hybrid models into healthcare DSS, the study also found important predictive criteria. Our results indicate that hybrid DL techniques, as opposed to conventional single-algorithm approaches, can greatly improve early disease identification and treatment procedures in healthcare settings.

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K. Tharageswari mail -

N. Mohana Sundaram mail -

R. Santhosh mail

link https://doi.org/10.54216/JCIM.160109

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

Cat-Feed-Nets: A Novel Cat Evoked Deep Feedforward Networks for Detection of Dos Attacks in IoT-Cloud Environment

Internet of things (IoT) is an intelligent combination of embedded systems, cloud computing and wireless communications. However, the data privacy and leakage problems are considered as the major deadlocks for deploying the IoT devices in the real time fields. Nevertheless, the complication of Distributed Denial of Service (DDoS) hazard on the IoT devices recent surge has seen an uptick, making it prone to numerous threat complications. For this reason, prompt detection of these attacks plays a pivotal role to safeguard the user’s data. The AI methodology of Machine and Deep Learning Models engaged for the designing the intelligent systems to provide the secured environment to safeguard the network against the various attacks. However, the computational overhead of deep learning model handicaps to deploy it in the IoT-Cloud environment. To tackle this issue, the present article suggests the novel hybrid learning based detection system called CAT-FEED-NETS that incorporates the Deep feed forward neural networks (DFFNN) where the hyper parameters are tuned by the Cat Swarm Intelligence Algorithms. Comprehensive trials and analysis are performed using NSL-KDD and UNSW datasets and criteria to assess the efficacy of quality measurements such as accuracy, precision recall, F1-score and model building time (MBT) is evaluated and analysed. Evaluation results are weighted against the various DL algorithms with the suggested model exhibiting better results than the other models by producing 0.96 accuracy, 0.956 precision, 0.955 recall and 0.9834s of MBT respectively. The proposed framework had proved its superiority in predicting the cloud attacks than the other existing frameworks.

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P. Jagdish Kumar mail -

S. Neduncheliyan mail

link https://doi.org/10.54216/JISIoT.160117

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

On Developing a Temporally Ordered Energy Efficient Routing Model (TO-EER) using Bio-Inspired Optimization for MANET

Mobile Ad-hoc Network is a structure of dynamic cellular network devices with no fixed architecture. Due to the network's constantly changing environment, characterized by frequent changes in its topology routing becomes a major challenge in MANET, which can reduce the overall network efficacy. As routing protocol plays a vital role in MANET, the energy-efficient routing model can enhance network longevity with a minimal rate of energy consumption. This paper uses a Temporally Ordered Routing Algorithm (TORA) to attain a higher scalability rate and an Elephant Herding Optimization (EHO) model to employ energy-efficient routing protocol features. The computations of the proposed model include the length of the route (LR) in optimal route selection and the energy level of routes (ER). It devises the routing problem as an optimization issue and further incorporates EHO for route selection, enhancing the weighted rate of LR and ER. The experimentations are carried out using the NS-3 simulation tool and factors such as latency, packet success rate, throughput, reliability, and energy depletion rate. Through a comparative analysis of the results with the previous works, the effectiveness of the proposed model is demonstrated.

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Hemalatha M. mail -

M. Mohanraj mail

link https://doi.org/10.54216/JISIoT.160118

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2024

A Study of Some Important Algorithms Used in the Process of Generating Random Numbers

The efforts of many researchers and scholars have focused on providing appropriate algorithms for generating random numbers and developing them in a manner that suits the need for them, but these algorithms still have advantages and disadvantages, so they are suitable for a specific study and not suitable for another study. The reason for the interest of researchers and scholars in the process of generating random numbers is that random numbers have many scientific and technical applications, starting with generating a series of semi-random numbers, starting from computer simulation to encryption, games of chance, and random samples for statistics and security. In simulation, which is one of the important methods provided by the new science of operations research, the primary reliance is on generating a series of random numbers that follow the regular distribution in the range [0,1], and then converting these random numbers into random variables that follow the probability distribution according to which the system to be simulated works, as the accuracy of the results we obtain from the simulation process depends on the numbers we generate using one of the algorithms. In other words, the appropriate algorithm for the field of study must be chosen from among the algorithms used, which prompted us to prepare this research, through which we will present a reference study of some of the algorithms used to generate random numbers. Where we will highlight the advantages and disadvantages of these algorithms and the most important areas of their use. Then we will calculate the number of these algorithms and compare them. The algorithms that we will discuss in this research are: ➢Middle Square Method. ➢Middle multi-Method. ➢Fibonacci Methods. ➢Linear congruential Methods.

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Sawsan Rateb almokabaa mail -

Maissam Ahamad Jdid mail

link https://doi.org/10.54216/PAMDA.040102

Volume & Issue

Vol. Volume 4 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2025

An Optimized Convolutional Neural Network for Alzheimer’s disease Detection

Alzheimer’s disease (AD) is a serious diseases distressing society. AD is a complex disease associated with many risk factors, such as aging, genetics, head trauma, and vascular disease. AD is also influenced by environmental factors such as heavy metals and trace metals. The pathology of AD, including amyloid-peptide (Aβ) protein, neurofibrillary tangles (NFTs), and synaptic loss, is still unknown. There are many explanations for the causes of AD. Cholinergic dysfunction is a main danger factor for Alzheimer's disease, whereas others believe that abnormalities in the production and treating of Aβ protein are the primary cause. However, there is currently no accepted hypothesis explaining the pathogenesis of AD. Magnetic resonance imaging is used to diagnose Alzheimer's disease. Our new AD pathogenesis showed 99.77% accuracy with 0.2% efficiency loss and outperformed VGG16, MobileNet2, and Inception V3 without the Adam optimizer and folder hierarchy.

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Amena Mahmoud mail -

Abdulaziz Shehab mail -

A. S. Abohamama mail -

Esraa Al-Ezaly mail

link https://doi.org/10.54216/JISIoT.160119

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Integrating Coot Optimization Algorithm with Deep Learning based Medical Image Analysis for Pancreatic Cancer Diagnosis

Pancreatic cancer (PC) is an extremely malignant cancer type with a maximum rate of mortality. It remains a challenging form of tumor to treat due to its late analysis and aggressive nature, which drastically decreases the survival rate. Early analysis of PC is vital for enhancing the probabilities of treatment and survival. PC analysis was initially dependent upon imaging, and then the recent imaging offered a worse prognosis, restraining clinicians’ treatment choices. PC detection utilizing deep learning (DL) contains the application of advanced computational methods for analyzing medical image data like CT scans or MRI images, for the early and correct detection of PCs. DL approaches, particularly convolutional neural networks (CNNs), are trained on huge databases for diagnosing forms and anomalies indicative of PC. Therefore, this study presents a novel Coot Optimization Algorithm with Deep Learning based Medical Image Analysis for Pancreatic Cancer Diagnosis (COADL-MIAPCD) technique. The main objective of the COADL-MIAPCD approach is to proficiently examine the medical images for the detection of PC. The COADL-MIAPCD technique primarily applies a median filtering (MF) for image pre-processing. In addition, the COADL-MIAPCD approach allowed using of an improved SE-ResNet. Moreover, the COA has been utilized for the optimum parameter choice of the improved SE-ResNet. At last, the extreme learning machine (ELM) has been used for the recognition and classification of PCs. The simulation outcomes of the COADL-MIAPCD technique has been validated utilizing a medical image database. The obtained experimental values stated that COADL-MIAPCD technique achieves better performance than other models.

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Eiman Talal Alharby mail

link https://doi.org/10.54216/FPA.190119

Volume & Issue

Vol. Volume 19 / Iss. Issue 1

Details open_in_new