Cloud IoT with Remote Sensing Data Segmentation and Classification Using Deep Learning Model for Sustainable Agriculture

T.; RM.; Gaddam Ravindra; T.; S.; S.; M.

doi:https://doi.org/10.54216/JISIoT.180118

Journal of Intelligent Systems and Internet of Things | 2026

IoT-Enabled Reversible Watermarking of Medical Images Using PCA and Hash-Based Signatures for Secure Smart Healthcare

The rise of IoT in smart healthcare systems necessitates secure and efficient methods to protect sensitive medical imaging data transmitted across interconnected devices. This research introduces a novel IoT-enabled reversible watermarking technique using Principal Component Analysis (PCA) and Hash-Based Signatures (HBS) to ensure both data integrity and diagnostic quality. The method supports secure embedding of watermarks into medical images captured and transmitted by IoT devices such as wearable scanners, remote diagnostic units, and edge sensors. By leveraging PCA for minimal distortion and reversible embedding, and HBS for robust tamper detection, the system ensures full restoration of original images post-verification. Discrete Wavelet Transform (DWT) further optimizes the compression and transformation for real-time IoT environments. The proposed approach demonstrates high imperceptibility (high PSNR), robust tamper detection (using SHA-256 and SHA-512), and full reversibility, making it ideal for real-time transmission of medical data over IoT-based healthcare networks.

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Pradeep Kumar Tripathi mail -

Manoj Varshney mail -

Aditi Sharma mail

link https://doi.org/10.54216/JISIoT.180109

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

A Distributed İntrusion Detection Using Long Short-Term Memory-Gradient Repeating Unit and Enhanced Density Peak Clustering for Real-Time Cyber Threat Detection

Due to the huge number of devices that connect to Internet of Things (IoT) networks, these networks have become the main nerve of the organizations that use them due to the large services that the networks provide to companies. In recent years, the number of attacks targeting IoT networks to shut down or violate data privacy has increased, so system developers must build strong protection systems to keep those networks secure. Intrusion detection systems (IDS) and intrusion prevention systems (IPS) are one of the most promising protection systems in securing these networks, but they suffer from several challenges, including high false positive alarms (FPA) and false negative alarms (FNA), in addition to the difficulty of controlling the long-time chains of incoming and outgoing traffic in IoT networks. This paper presents a distributed intrusion detection system (DIDS) based on the use of deep learning algorithms, specifically the enhanced long short-term memory (LSTM) algorithm with the gradient repeating unit (GRU) algorithm, as well as the use of a modern dataset collected from real network data called CICIOT2023. To adjust the threshold and achieve a balanced approach to the detection of anomalies, a hybrid model of the Enhanced Peak Density (DPC) aggregation algorithm with ROC curve analysis was used. The proposed work's main innovation is the combination of top-k feature selection with a hybrid LSTM-GRU architecture optimized for imbalanced datasets using focal loss, SMOTE, and dynamic class weighting. As a result, the intrusion detection pipeline is strong and effective. To evaluate the functioning of the system, standard performance metrics such as AUC-ROC, accuracy, F1-score, and recall were used, as the proposed system proved to be a powerful solution to prevent complex attacks targeting IoT networks as well as the possibility of detecting rare and modern attacks. The proposed model achieved promising results with accurate results reaching (96.0%) and a false negative rate (FNR) of 0.049% and a false positive rate (FPR) of 0.014%.

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Wisam Ali Hussein Salman mail

link https://doi.org/10.54216/JISIoT.180110

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2026

Arabic Fake News Detection Techniques: A Review

People are efficient on websites and social media platforms for news and updates as their popularity has grown. Even official media outlets to publish news use social media networks. However, due to the massive volume of user-generated material, verifying the veracity of the presented information is necessary. To handle the large volume of posts being made, this procedure should be implemented automatically and effectively. Fake news detection (FND) estimates the chance that a certain news story (news report, editorial, expose, and the like) is purposefully misleading. Over the past ten years, there has been an increase in interest in Arabic FND, and several detection techniques have shown some promise in identifying fake news across various datasets. This paper provides an overview of the fake news definition, consequences, detection strategies, and datasets that are utilized for detecting Arabic fake news. The design of Arabic FND systems is mainly based on two methods. The first one uses machine learning (ML) methods that rely on manually produced statistical data extracted from the text and used as a feature to distinguish between real and fake news. In the second strategy, “end-to-end” systems for detection are created using deep learning (DL) approaches. The investigation conducted in this paper may help researchers understand the advantages and uses of Arabic FND systems to develop more efficient algorithms in this field.

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Maysoon Ahmed Abbas mail -

Dhafar Hamed Abd mail -

Mondher Frikha mail -

Adel M. Alimi mail

link https://doi.org/10.54216/JISIoT.180111

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2026

Trustworthy and Interpretable AI in IoT-Based Medical Systems: A Review and Framework for CoT-XAI Integration

The use of Artificial Intelligence (AI) in medical diagnosis has rapidly evolved with the adoption of large language models and explainability techniques. This study investigates the intersection of Chain-of-Thought (CoT) reasoning and Explainable AI (XAI) in the development of trustworthy diagnostic systems, particularly within Internet of Things (IoT)-enabled healthcare environments. A systematic review of 106 Scopus-indexed publications (2016–2025) was conducted, supported by topic modeling (LDA) and keyword co-occurrence network analysis to identify dominant research themes and gaps. Findings reveal that while CoT and XAI are actively studied, their integration within real-time, distributed, and resource-constrained medical systems remains limited. Most research emphasizes either performance or interpretability in isolation, with minimal efforts to embed step-wise reasoning into deployable clinical AI pipelines. Moreover, few studies address how CoT can function effectively in edge computing or federated learning scenarios common to IoT infrastructures. To address this gap, we propose a multi-layered conceptual framework that integrates CoT reasoning, machine learning predictors, XAI methods, and IoT deployment models. This framework reflects the shift toward user-centric, transparent, and adaptive AI solutions in smart healthcare. It provides a structured path from multimodal data ingestion to clinically interpretable and real-time decision support. This study contributes a novel perspective on reasoning-driven explainability and offers design guidance for future development of interpretable, scalable, and deployable AI systems in medical applications.

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Faisal Binsar mail -

Sasmoko mail

link https://doi.org/10.54216/JISIoT.180112

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2026

Urban Planning Based Sustainable Public Healthcare System using Machine Learning Algorithms

Growing use of a wide range of Internet of Medical Things (IoMT) devices and apps makes smart health an increasingly vulnerable area. One popular method for creating smart city solutions that benefit vital infrastructures over time, such smart healthcare, is IoMT. Because Bluetooth technology is flexible and uses few resources, it is used for short-range communication by many IoMT devices in smart cities. This research proposes novel technique in urban planning in smart public healthcare system utilizing ML algorithms. The smart healthcare system is developed based on secure honeynet cloud IoT model. Here the input smart healthcare-based health monitoring data is collected and processed for missing value removal and noise removal. Then this data classified and optimized using recurrent Bi-LSTM temporal Gaussian model with whale swarm particle colony optimization. Experimental analysis is carried out in terms of detection accuracy, precision, data integrity, throughput, recall, latency. proposed technique obtained 96% of Detection    accuracy, 97% of Precision, 95% of Throughput, 88% of RECALL, 94% of LATENCY.

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V. Rajathi mail -

Pritee Parwekar mail -

V. Anantha Lakshmi mail -

M. Syed Rabiya mail -

M. Banu Priya mail -

V. Devi mail

link https://doi.org/10.54216/JISIoT.180113

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

An Adaptive Mutation-Aware Test Case Ordering Framework Using Deep Learning and Quantum-Behaved Multi-Objective PSO

  In regression testing, rapidly identifying defects is crucial for maintaining software quality amid frequent code changes. Traditional test case ordering methods, despite extensive research, often overlook the subtle but important relationship between test executions and mutations introduced during code modifications. This paper presents an adaptive mutation-aware test case ordering framework that integrates predictive modeling with swarm-based multi-objective optimization to address this gap. The approach begins by transforming test cases into enriched feature vectors, incorporating mutation coverage, historical performance, execution cost, and statement-level weighting. A supervised deep learning model is employed to predict the likelihood of each test case uncovering seeded defects. These predictions are subsequently fed into a Quantum-Behaved Particle Swarm Optimization (QPSO) engine, which generates an optimal execution sequence by jointly optimizing fault detection, execution cost, reuse potential, and coverage diversity. The proposed framework is demonstrated using a simple Java program and rigorously validated on real-world projects from the De-fects4J benchmark. Experimental results consistently show improvements in APFD, mutation scores, and execution efficiency, confirming the feasibility and scalability of the proposed system.

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S. Sowmyadevi mail -

Anna Alphy mail

link https://doi.org/10.54216/JISIoT.180114

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Satellite Imaging Based Risk Management in Cloud IoT Network Using Machine Learning Techniques

The consistent improvement of remote sensing (RS) technology has resulted in an easy access to a large volume of satellite imagery. There is a need for effective and scalable solutions for widening the application of RS in different fields and making it work efficiently in practical situations. This research propose novel technique in satellite image gathering and cloud IoT network risk management using machine-learning model. Here the cloud IoT network has been used in satellite image collection and this network security analysis has been carried out using secure trust based cryptographic blockchain model. Then this collected image has been classified using convolutional bayes fuzzy markov perceptron basis function model. Experimental analysis has been carried out in terms of accuracy, QoS, recall, latency, scalability. Proposed model attained accuracy of 97%, QoS of 94%, LATENCY of 96%, Scalability of 95%, RECALL of 93%. These results assist decision-makers, planners, and scientists studying remote sensing select an appropriate image classification system for tracking a dynamic, fragmented, and varied landscape.

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Jyotsnarani Tripathy mail -

T. Krishna Murthy mail -

S. Manjula mail -

Sukanya Ledalla mail -

Alla Rajendra mail -

P. Lakshmi Harika mail -

K Boopathy mail

link https://doi.org/10.54216/JISIoT.180115

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Diverse Geographical Region Analysis Based on Deforestation Rate Using Remote Sensing Image and Machine Learning Techniques

With direct implications for the regional climate, biogeochemistry, hydrology, and biodiversity, land cover change has been identified as one of the top priorities for the development of sustainable management plans. Among the primary causes of global warming are deforestation and forest fragmentation, which have profound effects on biodiversity preservation and ecosystem functioning. Machine learning techniques, like those employed in computer vision, have become widely used, making it possible to segment satellite images semantically to distinguish between areas that are forested and those that are not. This study presents a novel method for segmenting and classifying UAV images to detect deforestation using machine-learning models. In this case, noise reduction as well as normalisation is applied to input, which consists of UAV-based forest region photos. Semantic U-convolutional regressive neural network combined with deep radial quantile temporal neural network was then used to segment and classify this image. The suggested model's simulation analysis is assessed based on several metrics, including F-1 score, normalized coefficient ratio, average precision, AUC, and detection accuracy. proposed method yielded 97% detection  accuracy, 93% normalized coefficient ratio, 91% AUC, F-1 score of 94% and 95% AVERAGE PRECISION.

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Abhilash S. Nath mail -

Manu Gupta mail -

J. Sirisha Devi mail -

A Babisha mail -

D. Venkata Ravi Kumar mail -

B. Rama Subba Reddy mail

link https://doi.org/10.54216/JISIoT.180116

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Edge Cloud IoT Model Based Marine Life Analysis Using Machine Learning Algorithms

The amount of marine data is such that it is pointless, and at times infeasible, to attempt training deep learning models on personal workstations. In this work, we present the advantages of cloud based distributed learning in training of deep learning (DL) model and management of big data. Moreover, large volumes of marine big data are classically through wire networks, which are costly, if at all deployable, to maintain. This research propose novel technique in marine life analysis based on remote sensing image using edge cloud IoT model and machine learning algorithms. Here the edge cloud IoT model has been used for collecting remote sensing image in marine life analysis. This remote sensing image has been processed for noise removal as well as normalization. Then this image is feature extracted as well as classified utilizing principal Gaussian convolutional fuzzy encoder with Bayesian reinforcement Markova algorithm. Experimental analysis has been carried out in terms of classification accuracy, average precision, recall, F1 score, AUC for various marine life dataset. proposed technique obtained 97% Classification   accuracy, 95% Average precision, 93% Recall, 88% AUC, 94% F1 SCORE.

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Gagan Kumar Koduru mail -

S. Kalaimagal mail -

M. Srilakshmi Preethi mail -

G. L. Narasamba Vanguri mail -

Shivanadhuni Spandana mail -

M. Syed Rabiya mail -

M. Rajesh mail

link https://doi.org/10.54216/JISIoT.180117

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Cloud IoT with Remote Sensing Data Segmentation and Classification Using Deep Learning Model for Sustainable Agriculture

  Sustainable Development Goals of United Nations are focused on enhancing agricultural production that has the potential to be transformational at the local as well as the global level. The available technologies in agriculture management that are based on Internet of Things (IoT) encourage sustainable production of more food by farmers, which contributes significantly to the achievement of these SDGs. The aim of this research is to propose novel technique in sustainable agriculture field analysis based on cloud IoT model with remote sensing and deep learning model. Here the cloud IoT model is used in agriculture field based remote sensing data analysis. This image has been segmented using watershed K-means temporal neural network (WKMTNN) and classification is carried out using deep quantile regressive Boltzmann machine (DQRBM). The experimental analysis has been carried out in terms of random accuracy, average precision, sensitivity, specificity for various agriculture field dataset. Proposed model attained average precision 96%, sensitivity 93%, random   accuracy 98%, and Specificity 95%.  These results highlight the superiority of the moisture estimation framework against their regression-based counterparts.

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T. Shanmugapriya mail -

RM. Rani mail -

Gaddam Ravindra Babu mail -

T. Srinivasulu mail -

S. Saranya mail -

S. Gopinath mail -

M. Rajesh mail

link https://doi.org/10.54216/JISIoT.180118

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new