Effective Signal Transmission from Underwater to Air Utilizing Hybrid Communication Systems

Satea H.; Amjed Razzaq; Mahmood J.

doi:https://doi.org/10.54216/JISIoT.160219

Fusion: Practice and Applications | 2025

An Intelligent Fusion Framework of Deep Learning with Secretary Bird Optimization Algorithm for Named Entity Recognition in Arabic Language Texts

As increasingly Arabic textual data becomes accessible through the Intranet and Internet services, there is an important requirement for technologies and devices to handle the related data. Named Entity Recognition (NER) is an Information Extraction task that became a major part of several other Natural Language Processing (NLP) tasks. NER for Arabic has been obtaining improving attention, but possibilities for development in performance are even accessible. In recent decades, the Arabic NER (ANER) task has been confined to great effort to increase its performance. The ANER difficult task is to collect vast corpora or immense white gazetteers/lists that address probably the majority of Arabic language challenges like complexity, orthography, and ambiguity. Recently, deep learning (DL) has been the most typically applied NER model in the Arabic language and others. DL methods utilize the features of words and text to identify NEs. This paper presents a Secretary Bird Optimization Algorithm for Enhancing Fusion Deep Learning in Arabic Named Entity Recognition (SBOFDL-ANER) model. The main intention of the SBOFDL-ANER technique is to develop an effective method for NER in Arabic text. At first, the text pre-processing stage is applied to clean and transform the raw text into a structured format for analysis. Next, the word embedding method has been implemented by the Word2Vec method. Besides, the proposed SBOFDL-ANER technique designs ensemble models such as deep belief network (DBN), elman recurrent neural network (ERNN), and multi-graph convolutional networks (MGCN) for the process of classification. Eventually, the secretary bird optimization algorithm (SBOA) implements the hyperparameter choice of ensemble models. A wide-ranging simulation was applied to verify the performance of the SBOFDL-ANER method. The experimental outcomes demonstrated that the SBOFDL-ANER model highlighted improvement over other existing methods

groups

Ebtesam Hussain Almansor mail

link https://doi.org/10.54216/FPA.190227

Volume & Issue

Vol. Volume 19 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Integrating Tent Chaotic Dung Beetle Optimization with Deep Ensemble Learning for Diabetic Retinopathy Recognition on Fundus Imaging

Diabetic Retinopathy (DR) is a general difficulty of diabetes mellitus, resulting in retina damage that affects vision. If left undetected, it has the potential to cause blindness. Regrettably, DR is irreversible, and only treatment can maintain vision. The early analysis and treatment of DR can considerably decrease the potential for visual impairment. Unlike computer-aided diagnosis (CAD) systems, the manual diagnostics method of DR retinal images by ophthalmologists is effort-, cost-, and time-consuming and liable to misdiagnoses. In present scenario, deep learning (DL) has become the classical approach that has remarkable performance in different fields, mainly in medical image classification and analysis. Convolutional neural networks (CNN) are more commonly deployed as a DL system in medical image analysis and they are very efficient. In this manuscript, we offer the design of Tent Chaotic Dung Beetle Optimization with Deep Ensemble Learning for Diabetic Retinopathy (TCDBO-DELDR) Recognition approach on Fundus Imaging. The foremost intention of the TCDBO-DELDR technique is to automate the DR detection process on fundus images via the ensemble DL model. To eradicate the noise, the TCDBO-DELDR technique initially exploits the median filtering (MF) methodology. In the TCDBO-DELDR model, the Inception v3 (IV3) model is employed for the purposes of feature extractor. For the hyperparameter tuning procedure, the TCDBO technique is used for IV3 model. Finally, the detection of DR is carried out utilizing an ensemble of three classifiers namely Deep Feedforward Neural Network (DeepFFNN), Convolutional FFNN (ConvFFNN), and Convolutional bi-directional long short-term memory (ConvBLSTM). For ensuring the enhanced efficiency of the TCDBO-DELDR system in the DR detection procedure, a widespread experimental study is prepared on the benchmark DR database. The results illustrate the superior efficiency of the TCDBO-DELDR technique with other recent DL approaches.

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Arwa Darwish Alzughaibi mail -

Ashrf Althbiti mail -

Sultan Ahmed Almalki mail -

Mohammed Al-Jabbar mail -

Mohammed Alshahrani mail

link https://doi.org/10.54216/JISIoT.160212

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Blockchain with IoT Integrated Framework for Tourism Service Customization and Management

The Internet of Things (IoT) has extensively converted the industry of tourism, reforming travel design, supply, and experiences. The technology of Blockchain (BC) signifies a paradigm shift with the latent to transform many industries, more like spreadsheets altered office efficiency. BC technology provides frequent potential advantages to the tourism industry, with enhanced transparency, security, and efficacy in regions such as payments, bookings, and identity verification, which potentially mains to a more perfect and reliable travel experience. In the tourism region, BC with IoT is mainly attractive owing to the latent benefits it provides in terms of improving the experience of tourism, enhancing operational efficacy, and guaranteeing data security and transactions. Recently, numerous scholars globally have employed deep learning (DL) technology in the industry of tourism to combine physical and social influences for improved travel recommendation services. This study presents a Blockchain for Tourism Service Customization and Management using Whale‐goshawk Optimization Algorithm (BCTSCM-WOA) technique. The main goal of the BCTSCM-WOA method relies on improving the effectual model for tourism service customization. Initially, blockchain technology is applied to provide secure, transparent, and decentralized solutions for handling traveler data, payments, and service personalization. Then, the data pre-processing employs min‐max scaling to transform input data into a suitable format. Besides, the crayfish optimization algorithm (COA) to select the most relevant features from the data has executed the feature selection procedure. For the classification process, the proposed BCTSCM-WOA method projects multi-dimensional attention-spiking neural network (MASNN) technique. At last, the parameter tuning process is performed through the whale‐goshawk optimization (WGO) algorithm for refining the classification performance of MASNN model. The experimental evaluation of the BCTSCM-WOA algorithm has been examined on a benchmark dataset. The extensive outcomes highlight the significant solution of the BCTSCM-WOA approach to the classification process when compared to existing techniques.

groups

Samer Yaghmour mail

link https://doi.org/10.54216/JISIoT.160213

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

On the generalized numerical radii of operators

It is shown that if A, B,X, and Y are operators acting on a finite dimensional Hilbert space, then. ωu (AXB&lowast; ± BYA&lowast;) ≤ 2 ∥A∥ ∥B∥ ωu ([0 X, Y 0]) where ωu (T ), ∥T ∥, are, respectively, the U-numerical radius, the spectral norm, of an operator T .

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M. Abu Saleem mail -

Khalid Shebrawi mail -

Tasnim Alkharabsheh mail

link https://doi.org/10.54216/IJNS.260221

Volume & Issue

Vol. Volume 26 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

SCNN-UNet: A Novel Deep Learning Approach for Pulmonary Embolism Detection in COVID-19 Patients Using Super Pixel Segmentation

Inventory management is crucial for optimizing consumer demand and supply chains in e-commerce companies.  This is the stage at which precise inventory forecasting becomes necessary for forecasting future demand patterns and stock levels.  Traditional forecasting methods often struggle with e-commerce data due to seasonality, sudden changes in customer behavior, and nonlinearity.  Machine learning (ML) and deep learning (DL) techniques have become powerful weapons for inventory prediction because they can analyze huge amounts of data with high dimensionality. E-commerce firms can improve their resource allocation, inventory management, and customer experience in highly competitive market environments.  This paper proposes different types of inventory forecasting models and mainly evaluates the applicability of sophisticated machine learning algorithms.  While we commonly use old methods like Random Forest, ARIMA, and MLPs, they often lack the necessary robustness to nonlinearity within inventory data.  To address these problems, we introduce a novel method that combines convolutional neural networks (CNN) and XGBoost called CNN-XGBoost, which provides better feature extraction than the conventional prediction model and regression performance.  We then compared CNN-XGBoost's performance to traditional forecasting methods (another common approach to contextualizing predictive model performance) using a 52-week simulated dataset in which we mimic patient data growing over time.  We used key performance metrics such as R2, mean squared error (MSE), and mean absolute percentage error (MAPE) to assess each model's accuracy.  The CNN-XGBoost model performed much better than others, with an R2 of 0.78, which means our proposed model can explain more variation compared to other competitors, as depicted in the results section.  It also had the best MSE of 0.15, indicating better predictive performance.  The CNN-XGBoost model demonstrated promising prospects as a robust inventory forecasting tool for commerce despite its slightly higher MAPE value (0.69), suggesting some vulnerability to outlier data points.  This study demonstrates the potential of using a convolutional neural network in combination with gradient boosting techniques to tackle the complexity of stock management issues and the fact that it outperforms based line methods by a large margin.

groups

Sukhwinder Bir mail -

Vijay Dhir mail

link https://doi.org/10.54216/JISIoT.160214

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Alzheimer Detection Using Deep Learning Methods

This study proposes a&ensp;deep learning-based framework to detect and classify Alzheimer's disease (AD) in the early stages using medical imaging, and specifically Magnetic Resonance Imaging (MRI). Specifically, we propose a Convolution Neural Network (CNN) based model and transfer learn (MobileNet) through pre-trained models based on&ensp;task domain to improve model performance on binary AD classification. Thanks to minimizing computational complexity and memory costs, the model with 99.86% accuracy rate can mitigate overfitting and&ensp;is an ideal approach for real time and eco-friendly monitoring of AD evolution. The findings suggest that the model could help&ensp;clinicians in diagnosing AD even based on MRI images, which has great potential as a scalable and efficient solution for the early-stage diagnosis and classification of the disease. Our work will include the addition of further pre-trained models, increased dataset size via data augmentation, and the application of MRI segmentation to better isolate some of the key&ensp;features of Alzheimer.

groups

Raghad K. Mohammed mail -

Mohammed Q. Jawad mail -

Othman Mohammed Jasim mail

link https://doi.org/10.54216/JISIoT.160215

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Enhancing Osteoporosis Detection with Fuzzy Logic Preprocing and Pre-Trained Deep Convolutional Neural Networks

This study investigates combining fuzzy logic with deep learning methodologies in classifying X-ray images for osteoporosis detection. Osteoporosis, defined by compromised bone integrity and heightened fracture susceptibility, requires prompt and precise diagnosis for effective treatment. We devised a hybrid approach that amalgamates transfer learning from Convolutional Neural Network (CNN) architectures, including MobileNetV2, AlexNet, ResNet50V2, and Xception, utilizing fuzzy logic during the preprocessing phase to address uncertainty and imprecision in X-ray images, thereby enhancing the quality of the input data for the subsequent pre-trained models. The research entailed the examination of a significant dataset of X-ray images and the implementation of the proposed methodology to categorize images as osteoporotic or non-osteoporotic, attaining a remarkable accuracy of 99.68% and a receiver operating characteristic (ROC) of 100% through the integration of fuzzy logic preprocessing with ResNet50V2. This innovative method may substantially decrease diagnostic inaccuracies and enhance patient outcomes, facilitating additional research and development in applying deep learning techniques in healthcare.

groups

Woud Majid Abed mail -

Murtadha M. Hamad mail -

Azmi Tawfeq Hussein Alrawi mail

link https://doi.org/10.54216/JISIoT.160216

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

A Hybrid Encryption Model with Blockchain Integration for Secure Cloud Data Storage and Retrieval

  Data security, privacy, sensitivity, and integrity are major concerns when using cloud-based storage solutions. In this paper, we propose a hybrid encryption model that has been integrated with blockchain technology to secure data storage in the cloud. The proposed model facilitates data encryption using a symmetric cryptography algorithm for efficient large data encryption while ensuring the encryption key can only be exchanged using asymmetric cryptography. This model utilizes the power of blockchain to manage metadata securely and associated encryption keys to ensure that records are tamper-proof, removing the need for third parties to be trusted. The security, key management, and data integrity of the proposed model are better than traditional cloud storage and existing blockchain-based approaches. The performance evaluation suggests that the model achieves a balance between security and cost efficiency, while moderate transaction speed will be witnessed owing to blockchain operations. Our proposed work aims to provide a scalable, fast, reliable, and decentralized architecture-based solution to address the challenges of cloud data security.  

groups

Firas Mohammed Khalaf mail -

Ali Makki Sagheer mail

link https://doi.org/10.54216/JISIoT.160217

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Efficient Plant Disease Detection Using Lightweight Deep Learning Model

Early detection of plant diseases is critical to minimizing their adverse effects&ensp;on agricultural productivity. In particular, machine vision and deep learning approaches (e.g., convolutional neural networks, CNNs) have been increasingly applied for automatic&ensp;plant disease identification. Although existing deep learning models achieve satisfying classification accuracy, they often consist&ensp;of millions of parameters that significantly lead to the lengthy training time, prohibitive calculation costs and deployment obstacles at the resource-constrained edge devices. In order to overcome those constraints, we introduce a new deep learning architecture, which uses adaptations of Inception layers and residual&ensp;connections that can help both with feature extraction and efficiency. In addition, depthwise separable convolutions are used to drastically reduce the amount of&ensp;trainable parameters with small loss of representational power. We perform training&ensp;and evaluation of the proposed model on three located benchmark plant disease datasets, PlantVillage dataset, the Rice Disease dataset. Experimental results show that our model achieves state-of-the-art classification accuracy of 99.39% on the PlantVillage dataset, 98.66% on the Rice&ensp;Disease dataset. In contrast to the state-of-the-art deep learning models, our method obtains higher accuracy with fewer&ensp;parameters so that it could be better suited for real-time applications on mobile and embedded systems. We explore an application of deep learning with the use of optimized architectures and present the viability of this technique in&ensp;precision agriculture for faster and more accurate diagnosis of diseases in plants with lower computational load.

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Abdalrahman Fatikhan Ataalla mail -

Karam Hatem Alkhater mail -

Qusay Hatem Alsultan mail -

Zaid Sami Mohsen mail -

Munther Naif Thiyab mail -

Mohammed Waheeb Hamad mail -

Ahmed Jumaah Yas mail

link https://doi.org/10.54216/JISIoT.160218

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Effective Signal Transmission from Underwater to Air Utilizing Hybrid Communication Systems

Underwater optical communication (UOC) and off-surface areas wireless communications are a rapidly growing field, especially with the emergence of new technologies such as autonomous underwater vehicles and above/water drones. The challenge lies in the absence of a water surface platform to transfer the signal from underwater to off surface. This research investigates the design and implementation of a hybrid communication system that successfully transmits signals from underwater environments to above-water. The study utilizes OFDM as method to generate data on the integration of underwater optical wireless communication (UWOC) at 532nm and LOS optical channel. After adjusting the line of sight through the angle of refraction and overcoming the challenges of water and above water conditions as well as ambient lighting, ambitious results were obtained 100 meters above clear water and 40 meters in haze wither at a depth of 10 meters for transmission. The research has mitigated challenges and enhancing the effectiveness of underwater-to-air communication systems.

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Satea H. Alnajjar mail -

Amjed Razzaq Alabbas mail -

Mahmood J. Ahmad mail

link https://doi.org/10.54216/JISIoT.160219

Volume & Issue

Vol. Volume 16 / Iss. Issue 2

Details open_in_new