Deep Learning Approaches for Automated Disease Detection in Agriculture

Ahmed A. F.; Rajit; Mosleh Hmoud; Theyazn H.H; Saad M.; Sami A.

doi:https://doi.org/10.54216/FPA.200204

Journal of Intelligent Systems and Internet of Things | 2025

Smart System to Enhance Medical Examinations Data Analysis for University Students

Mobile health (mHealth) applications have revolutionized the healthcare sector by providing innovative solutions for patient monitoring, health tracking, and medical consultation. These applications leverage the widespread use of smartphones to deliver health services that are accessible, affordable, and efficient. Research indicates that mHealth technologies significantly improve healthcare service delivery processes, enhancing patient outcomes and healthcare management. Furthermore, the functionality of mobile apps in health interventions has been systematically reviewed, showing positive impacts on user engagement and behavior change. This study explores the development and implementation of a medical screening application for incoming university students using an Android platform. The application is designed to perform basic health check-ups, including monitoring and assessing general health status, and providing recommendations for further medical consultation if necessary. The application includes several modules: blood test analysis, vision test, hearing test, and speech test. By leveraging advancements in mobile health (mHealth) technologies and artificial intelligence, the application offers a cost-effective and scalable solution for university health services. This paper highlights the potential benefits, challenges, and future implications of deploying mobile health screening applications in educational institutions.

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W. K. ElSaid mail -

Basma E. Seif mail -

Ahmed Abd El-Badie Abd Allah Kamel mail

link https://doi.org/10.54216/JISIoT.170201

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Cloud-Enabled Assistive Robotics System for Secure and Interoperable Internet of Medical Things Ubiquitous

The current landscape of assistive robotics in digital healthcare faces significant challenges, particularly in ubiquitous environments. Existing systems need the necessary infrastructure to monitor and process data, hindering their effectiveness. Moreover, the arrangement and management of IoMT (Internet of Medical Things) data across various nodes present a new challenge, further complicating the deployment of assistive digital healthcare solutions. We propose a novel Assistive Robotics-Based Digital Healthcare System within a Ubiquitous IoMT Cloud network to address these challenges. This system supports various medical care applications, including digital wheelchair location tracking, artificial limbs, and remote surgical operations across different hospitals. Our contributions are as follows: We introduce the ARDTS (Assistive Robot Digital Healthcare Task Scheduling) algorithm to efficiently process data across multiple nodes; ensuring secure data handling based on the systems security protocols. We implement a convolutional neural network for data standardization, converting non-linear data into a linear form to predict relevant features accurately. We develop a socket-enabled cross-platform system to enhance interoperability for seamless data sharing and processing.

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Ahmed Ali Alhammad mail -

Israa Badr Al-Mashhadani mail -

Marwa K. Farhan mail -

Mazin Abed Mohammed mail

link https://doi.org/10.54216/JISIoT.170202

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Countermeasure to Black Hole Attack in MANET Wireless Network Security

Establishing basic network connectivity by mobile devices depends on wireless communication during infrastructure downtime. Nodes within these networks use routing protocols to send data packets between one another until the packets reach their endpoint. The protocols have security weaknesses that permit harmful nodes to stage assaults on the network. Network disruption occurs through the Black Hole Attack, which blocks all data packets from getting to their destinations by intercepting them during their transmission. Security systems that detect intruders executing these attacks protect against the security challenge. A simulated wireless ad-hoc network scenario is the basis for assessing how well response systems fight against the Black Hole attack. In this paper, the Anti-Black Hole Ad hoc On-Demand Distance Vector (ABAODV) is the proposed solution to combat the Black Hole attack effects. During the experiments, ABAODV's modified AODV version and standard AODV protocol underwent performance measurements through throughput, Packet Delivery Fraction (PDF), Average End-to-End Delay (AED), and Normalized Routing Load (NRL) while operating in Black Hole attack environments and without such attacks. Through its NS-2 implementation, ABAODV achieved 99% effectiveness in combating the Black Hole attack. The entire simulation was conducted on a Linux platform, including mobility generation, analysis, results presentation, and NS-2 simulation.

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Bahaa Kareem Mohammed mail -

Hayder Najm mail -

Mohammed Salih Mahdi mail -

Riyadh Rahef Nuiaa Alogaili mail -

Waleed Khaled mail

link https://doi.org/10.54216/JISIoT.170203

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Comparative Analysis of Machine Learning Models for Predictive Healthcare in Chronic Disease Management

This study investigates the application of AI-powered predictive analytics in chronic disease management, focusing on the most effective machine learning models for predicting patient risk and optimizing healthcare interventions, like Random Forest, Linear Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting were evaluated using a dataset of 10,000 patient records. The models were assessed based on their accuracy, interpretability, and clinical relevance. Gradient Boosting attained the highest predictive accuracy, with an AUC of 0.89. Random Forest followed closely with an AUC of 0.85, offering a good balance of accuracy and interpretability. Linear Regression, with an AUC of 0.75, demonstrated the trade-offs between simplicity and model performance, while SVM and KNN performed with AUCs of 0.82 and 0.78, respectively, with SVM being robust but facing scalability challenges and KNN being less practical for large datasets. These AI models improve patient outcomes, decrease healthcare costs, and optimize healthcare delivery.

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Dena Kadhim Muhsen mail -

Bushra Fuaad Khmas‎‎ mail -

Amjed Abbas Ahmed mail -

Ahmed T. Sadiq mail

link https://doi.org/10.54216/JISIoT.170204

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Efficient Spam Email Detection Model based on Dynamic Embedding with Deep Learning Classification

One of the major concerns when transitioning emails is the potential influx of unsolicited and unwanted spam emails. These unwanted emails can clog inboxes, causing recipients to overlook important messages and opportunities. To ensure security and avoid the destructive and dangerous effect of these spam emails, machine learning and deep learning methods have been conducted to design spam detection models. In this work, a combination of embedding models and multi-layer artificial neural networks as deep learning classification models is utilized in order to introduce an approach to spam detection. The proposed classifier leverages the Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, applied to the Enron-Spam dataset, offering a noteworthy technique for considerable spam detection. Experimental results demonstrate that the proposed spam detection model achieved a 99% recall rate for detecting spam emails. Notably, this model is a step forward in generality and improving the efficiency of spam detection. It presents a good attempt at presenting a solution for detecting spam emails and fake text within communication environments.

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Salam Al-augby mail -

Zahraa Ch. Oleiwi mail -

Hasanen Alyasiri mail -

Fahad Ghalib Abdulkadhim mail

link https://doi.org/10.54216/JISIoT.170205

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

An Empirical Evaluation of the Main Factors of a Cybersecurity Culture in South African E-health Institutions Using Multiple Linear Regression

E-health institutions are prominent targets for cybercriminals due to their reliance on information technology systems and issues related to the users have been identified as the biggest security weakest. Hence, while cybersecurity culture (CSC) research emphasizes the necessity of the human factor, limited empirical work has been done in the context of e-health in Africa. Therefore, an empirical evaluation was conducted to identify how preparedness, responsibility, management, technology and environment influence cybersecurity in South African e-health institutions. This quantitative research studied e-health institutions in the Mpumalanga province of South Africa. Various methods were used to investigate the multiple linear regression effects of the main factors of CSC and the results show that although the preparedness (Beta = 0.281; p-value < 0.05) and environment (Beta = 0.500; p-value < 0.05) factors had the greatest influence, management, technology and environment had a positive effect on CSC. These factors contributed 48.2 % to the variance (R-Squared). The study seems to be the first empirical study that combines the human factor domain framework (HFD) with other theoretical frameworks to identify critical factors of CSC. Furthermore, the impact of technology on CSC was empirically tested. The study is significant as it identified key factors that contributed to the institution’s CSC and quantified their impact. These results can enable e-health institutions to make decisions based on evidence regarding their cybersecurity interventions, strategy and practices. However, the empirical evaluation was limited to one context, namely the Mpumalanga province in South Africa and at two hospitals selected based on easy access (convenience) and purposive sampling with criteria based on work experience and knowledge of CSC limited the number of participants eligible to participate.

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Nwanneka E. Mwim mail -

Jabu Mtsweni mail -

Bester Chimbo mail

link https://doi.org/10.54216/JCIM.170213

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Advanced Predictive Analysis of EGDI Time Series Using Hybrid ARIMA-LSTM and SARIMAX: A Comparative Study for Iraq and Tunisia

This study presents a predictive modeling framework for forecasting the E-Government Development Index (EGDI) using two advanced time series approaches. Firstly, the Seasonal Auto Regressive Integrated Moving Average with Exogenous Variables (SARIMAX). Secondly, hybrid ARIMA-LSTM model. We focus on two case studies, Iraq and Tunisia, based on monthly EGDI data from the United Nations Survey Reports, spanning the years 2003 to 2024. Using several preprocessing steps such as handling missing data, testing for stationarity using the combined ADF and KPSS tests, and determining the optimal ARIMA parameters through ACF and PACF analysis and implementing autoarima. The model was built and trained using 80% of the data, while 20% was retained for testing. The independence of the residuals verified using the Ljung-Box test. Four types of visualization and error analysis were applied using ACF/PACF for residuals, error plots as prediction error plot, error distribution plot (histogram + KDE) and decomposition analysis to visually assess model fit. Evaluation was conducts using multiple error metrics, including RMSE, MAE, MAPE, MHE, AIC, BIC and MAPA. After building the four models, we ensured that the results and reconstructions were evaluated using the 12 tests we mentioned, and that they were based on the best results and were consensus acceptable. ARIMAX model demonstrated superior performance, achieving an average absolute percentage Accuracy (MAPA) of 98.35% for Iraq and 97.93% for Tunisia. In comparison, the hybrid ARIMA-LSTM model, which combines linear ARIMA outputs with nonlinear corrections from an LSTM neural network, demonstrated competitive predictive ability with a MAPA of 95.68% for Iraq and 96.14% for Tunisia.  SARIMAX showed slightly outperformed the hybrid model in overall accuracy. On other hand, ARIMA-LSTM model demonstrated robustness in capturing complex nonlinear dynamics particularly in the more structurally diverse Tunisian dataset. These results confirm the potential of both models as effective tools for predicting EGDIs and support their application in digital governance planning and policymaking. We designed and we recommend adopting our "12 -Test Approach" for evaluation framework as a standard methodology in future studies addressing analysis and forecasting, and its suitability for different types of time series models. This approach provides comprehensiveness, accuracy, and flexibility in evaluation, regardless of model type or application area.

groups

Ali Ahmed Ali mail -

Atef Masmoudi mail

link https://doi.org/10.54216/JISIoT.170206

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Designing Explainable Deep Learning Models for Biomedical Data Analysis and Clinical Prediction Enhancement

Recent advancements in biomedical data analysis have significantly transformed clinical decision-making. However, the inherent complexity and heterogeneity of healthcare data continue to present major challenges. Traditional deep learning models, while powerful, often lack transparency, limiting their adoption in clinical settings due to their "black-box" nature. To address this critical gap, this study introduces a novel Explainable Deep Learning (XDL) framework that integrates high predictive accuracy with interpretability, enabling clinicians to trust and validate AI-driven insights. The proposed framework leverages advanced interpretability techniques—such as Grad-CAM for visual attribution and SHAP for feature importance analysis—to analyze multimodal biomedical data, including clinical imaging, genomic sequencing, and electronic health records. Experimental evaluations across three benchmark datasets demonstrated the model’s strong performance, achieving an accuracy of 91%, sensitivity of 95.4%, specificity of 98.6%, and an AUC of 99%, while maintaining an interpretability score of 92% as rated by domain experts. Compared to non-explainable models, the proposed approach showed a 12.3% increase in interpretability and a 5.8% improvement in accuracy. Importantly, attention map analysis revealed alignment with clinically relevant biomarkers in 93% of cases and uncovered previously overlooked prognostic patterns in 18% of patient cohorts. These findings underscore the model’s potential to enhance diagnostic precision and support more informed clinical decisions. Moreover, the algorithm reduced diagnostic time by 23% due to its provision of actionable insights. The hybrid approach—combining built-in attention mechanisms with external interpretability tools—ensures seamless integration into clinical workflows while supporting compliance with regulatory standards for transparency.

groups

Maha Rahrouh mail -

Walid Alayash mail -

Inas salah Mahmoud mail -

Marwa Hussien Moahmed mail

link https://doi.org/10.54216/JISIoT.170207

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Ensemble Learning-Based Intrusion Detection and Classification for Securing IoT Networks: An Optimized Strategy for Threat Detection and Prevention

The Internet of Things (IoT) advancement has created new security holes, which require intrusion detection systems to defend networks effectively. The complex structure of IoT networks causes traditional security methods to fail because they produce high amounts of incorrect detections and limited ability to accurately identify threats. The authors introduce ID-ELC: Ensemble Learning and Classification framework for Intrusion Detection, which aims to strengthen IoT environment security. A new ID-ELC model uses CS optimization with composite variance to choose network features that boost their detection capabilities. The cybersecurity evaluation of the system utilized Kyoto network records that included 91,000 intrusion-prone records and 59,000 benign logs from 150,000 total records. Experiments revealed ID-ELC surpasses Statistical Flow Features (SFF) and Two-layer Dimension Reduction and Two-tier Classification (TDRTC) through precision 0.98, accuracy 0.98, sensitivity 0.99 and specificity 0.97. Science-based evaluations confirm ID-ELC represents a flexible and resilient tool for IoT intrusion protection that shows practical value for citywide security systems and medicine networks and manufacturing operations. Future investigation will concentrate on enhancing the selection of features alongside classification methods to address rising cyber threats.

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Kumaresh Sheelavant mail -

Charan K. V. mail -

B. Yamini Supriya mail -

Purshottam J. Assudani mail -

Chandra Bhushan Mahato mail -

Sanjay Kumar Suman mail

link https://doi.org/10.54216/JISIoT.170208

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2025

Deep Learning Approaches for Automated Disease Detection in Agriculture

This research introduces a cutting-edge deep learning-based agricultural engineering illness diagnosis approach. Convolutional neural networks (CNNs) and improved methods improve accuracy and efficiency. The recommended solution includes network settings, convolution processes, and sharing strategies to reduce dimensions. These methods reduce the network's processing power so it can concentrate on disease characteristics. The model employs dropout regularization, attention processes, and multi-scale feature extraction to enhance sickness prediction. The technology also utilizes photographs and sensor data to adapt to agricultural circumstances. The performance test shows that the suggested technique outperforms traditional machine learning and mixed models in F1 score (95%), accuracy (95%), precision (94%), memory (96%), and correctness (94%). It has high discriminative power with an AUC-ROC score of 0.98. The model uses computers well: two hours to train, two seconds to derive conclusions, and 65% of the CPU at all times. Real-time farming could benefit from its use. The suggested technique can properly and reliably diagnose illnesses due to its low overfitting rate and excellent generalization potential. The precision agriculture technique will enhance crop health management and productivity.

groups

Ahmed A. F. Osman mail -

Rajit Nair mail -

Mosleh Hmoud Al-Adhaileh mail -

Theyazn H.H Aldhyani mail -

Saad M. AbdelRahman mail -

Sami A. Morsi mail

link https://doi.org/10.54216/FPA.200204

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new