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        <title>International Journal on Smart Sensing and Intelligent Systems Feed</title>
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            <title>International Journal on Smart Sensing and Intelligent Systems Feed</title>
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        <copyright>All rights reserved 2026, International Journal on Smart Sensing and Intelligent Systems</copyright>
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        <item>
            <title><![CDATA[Developing a Traffic Monitoring and Control Model for Communicat ion Channels of TCCNs]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0078</link>
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            <pubDate>Fri, 16 Oct 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This study investigates the technical characteristics of controllers operating at the lower layers of cyber-physical systems, known as industrial networks, which are designed for reliable long-term operation in harsh environments and are widely deployed in such systems. Monitoring and controlling communication traffic in technological corporate computer networks (TCCNs) has become a critical challenge. To address this issue, traffic behavior is analyzed at both the lower level, where industrial networks connect primary transducers and object controllers, and the upper level, which interfaces with corporate networks. Experimental results show that traffic volume in industrial networks remains nearly constant over time, whereas segments closer to corporate networks exhibit periodic patterns associated with daily human activities. Based on these observations, appropriate traffic control algorithms and methods are proposed for both scenarios. Furthermore, a traffic control model is developed for the periodic case, and its analytical solution is presented to support efficient network management.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Predicting Sepsis in ICU Patients Using GRU-D and LightGBM for Clinical Decision Support]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0072</link>
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            <pubDate>Thu, 08 Oct 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Sepsis is a life-threatening and acute condition, which is caused by dysregulation in host responses to infection and which can frequently result in organ failure and a high mortality rate in intensive care units (ICUs). Sepsis should be identified early enough to allow timely interventions; however, the conventional scoring system can be limited in accuracy and in its capacity to deal with complex temporal clinical data. This paper proposes a hybrid artificial intelligence system comprising of a gated recurrent unit with decay (GRU-D) and light gradient boosting machine (LightGBM) to predict sepsis in ICU patients. GRU-D model is developed to model the temporal dynamics of the multivariate time-series clinical-time data and can well take into consideration irregular sampling and missing data, whereas LightGBM can use managed features and non-linear interactions to advance predictive performance. The ICU electronic health record data was carefully preprocessed to keep the model reliable and to avoid any information leakage. The hybrid model showed better predictive performance than the standalone GRU-D or LightGBM models, and a better area under the precision-recall curve, higher sensitivity, and earlier sepsis onset. The results show that the hybrid GRU-D + LightGBM model outperforms the individual GRU-D and LightGBM models, achieving the highest accuracy (0.888 ± 0.011), precision (0.857 ± 0.017), recall (0.845 ± 0.020), F1-score (0.851 ± 0.018), and Area Under the Receiver Operating Characteristic Curve (AUROC) (0.931 ± 0.009). Besides, feature importance and SHAP value analyses through interpretability yielded insights into clinically significant predictors of the model, which increases transparency of the model to healthcare providers. The suggested GRU-D and LightGBM model can provide a convenient and effective clinical decision support system that could help clinicians identify and treat sepsis in time and potentially enhance patient outcomes and decrease ICU mortality.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Real Time Heart Rate Variability Measurement Based on Photoplethysmography Signal Using Smart Phone Application]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0049</link>
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            <pubDate>Fri, 02 Oct 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The role of real-time health monitoring systems is to manage patient health and chronic diseases by applying sensor-based technologies to health management. This study aims to design an effective smartphone-based system to measure real-time heart rate variability (HRV) by means of photoplethysmography (PPG) signals, addressing the drawbacks of motion artifacts, physiological differences, computational limitations, and overestimation errors currently associated with other techniques.Methods/AnalysisWithin the proposed system, the fingertip videos are recorded with a smartphone camera, and the PPGs are extracted by analyzing the red, green, and blue (RGB) channels. Preprocessing techniques are used to process the red channel intensity, and signal enhancement is performed by bandpass filtering and fast Fourier transform (FFT). Systolic peaks are detected using peak picking, classification, and HRV estimation is done by using adaptive RGB thresholding and moving variance methods.FindingsThe designed system is capable of measuring heart rate and HRV in real time with better accuracy. The experimental findings prove that the method suggested has a high probability of performance with a low error (~5.97%) rate as compared to a conventional pulse oximeter. The comparison made on the basis of mean, standard deviation and power spectral density (PSD) indicates consistency and validity in the estimation of HRV.Novelty/ImprovementThe proposed model presents a low-complexity real-time HRV monitoring approach on a smartphone that does not require any further hardware. Strong peak detection, adaptive thresholding, and RGB-based PPG extraction are integrated to make it robust and computationally efficient and applicable to real-life applications in healthcare. Nevertheless, measurements of HRV in real-time by smartphone-based PPG signals are associated with a number of limitations such as motion artifacts, changes in illumination, device-specific variations in RGB signals, and signal acquisition noise. This paper aims to solve these problems by proposing an efficient framework that uses adaptive RGB thresholding, motion artifact reduction, and strong signal processing methods. The significant contribution of the work is that it developed a real-time, low-calculation, and device-independent system of monitoring HRV on a smartphone platform, which guarantees a better accuracy and reliability in the practical conditions.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An efficient AI model for highway vehicle speed limit violation detection and real-time law-enforcing functionality]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0076</link>
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            <pubDate>Tue, 22 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Traffic control at toll exit booths is unable to promptly apprehend speeders. Therefore, speeders get more self-assurance for overspeeding. According to different studies, data loss and transportation errors are significant challenges for traffic management systems. It significantly impacts data retrieval and anticipated outcomes. The integrity of data collection may be impacted by partial or whole data loss due to distinct transit problems. Consequently, due to the distorted data, the accuracy of detecting the overspeeding cars or drivers may be disrupted. However, the current literature fails to offer any integrated approach to resolve such issues. Therefore, this study offers an improved method for educating drivers against speeding. A challan is automatically issued following three highway speed offenses. It notifies the motorist before issuing the citation. The neighboring integrated network devices relay challan information to the traffic control office as the motorist passes the toll booth, traffic signal, or roadside site. Consequently, enhancing the psychological influence of exit booth enforcement on offenders to ensure compliance with payment. These psychological impacts dissuade speeders and augment fines. This project essentially involves a real-time, Internet of Things (IoT)-based artificial intelligence (AI) methodology for mitigating highway speeding. Enhanced simulated annealing (SA) improves the detection of speed limit violations and ensures data integrity. It features an effective error control mechanism to reduce transmission errors and data loss. The experimental results indicate that it offers greater predictive accuracy for speeds, displaying improved precision, recall, F1-score, and area under the ROC curve (AUC) score relative to current methodologies. Its power to produce improved information processing and diminished cyclomatic complexity (CC) confirms its temporal efficiency. The diminished information percentage, along with packet loss and an increased signal-to-noise ratio (SNR) value, illustrates its capacity to enhance data integrity.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Modeling Rating Uncertainty with Interval Type-2 Fuzzy Decision Trees in Multi-Criteria Recommendation]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0074</link>
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            <pubDate>Mon, 21 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Managing uncertainty in user-generated data is a persistent challenge for multi-criteria recommender systems. This study introduces a redundancy-based interval type-2 (IT2) fuzzy approach that models rating ambiguity across five movie evaluation criteria—overall, story, acting, direction, and visuals. Using Histogram-Based Interval Type-2 Fuzzy Membership Functions (HBIT2FMFs), the method captures both membership values and their uncertainty bounds. User rating vectors are first clustered with hierarchical density-based spatial clustering of applications with noise (HDBSCAN) to identify dense regions, then refined via k-means into subclusters that serve as categorical targets for interval type-2 IT2 fuzzy ID3 and C4.5 decision trees. Evaluations on the Yahoo! Movies dataset show consistent improvements in MAE, precision, recall, and F1-score over crisp and type-1 models. Personalized recommendations are generated by computing cosine similarity within each subcluster. Overall, the framework provides an interpretable, robust, and uncertainty-aware solution for multi-criteria collaborative recommendation.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Detecting Deepfakes via Robust Spatio-Temporal Residual Learning Using Variational Autoencoders and EfficientNetV2]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0036</link>
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            <pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Deepfakes are a significant threat to digital trust, biometric authentication, and the reliability of online content. Current deepfake detection tools, which are exclusively based on the raw spatial factors are characterized by poor cross-dataset generalization and prone to compression artifacts. To address these shortcomings, this paper presents a robust spatio-temporal residual learning framework to detect deepfakes in images and videos. Variational autoencoder is trained on real images with the adversarial perturbation training using fast gradient sign method (FGSM) and projected gradient descent (PGD) to learn stable and manipulation-sensitive residual maps. In the case of image-based detection, an EfficientNetV2 fine-tuned model is used to classify residual maps. To analyze video, a temporal residual consistency (TRC) module, which is based on a GRU or 1D-CNN model, can be used to capture inter-frame residual inconsistencies. The proposed method achieves 93.8% accuracy and 0.96 AUC on FaceForensics++, 80.1% cross-dataset accuracy on WildDeepFake, and improves video-level detection by 5.1%, demonstrating strong robustness and generalization.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Faultformer: A CNN-Transformer with Multi-Domain Feature Fusion Network for Intelligent Bearing Fault Diagnosis]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0075</link>
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            <pubDate>Sun, 20 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[In this paper, FaultFormer is proposed as a new hybrid intelligent mechanism for automatically diagnosing rolling bearing faults using vibration signals from an induction motor. It combines multi-domain feature fusion based on handcrafted statistical features, frequency-domain analysis, and deep features learned through a hybrid convolutional neural networks (CNN)-transformer-based approach. Whereas CNN layers capture local temporal vibration patterns, the transformer encoder leverages multi-head self-attention to capture long-range temporal dependencies that are important for identifying periodic mechanical faults. The framework was validated using the Case Western Reserve University bearing dataset, which covers 10 fault categories across different load modes and defect severities. Experimental results show that the model achieves 99.98% classification mean accuracy across 20 runs, with a precision of 99.68%, a recall of 99.68%, and an F1-score of 99.80%, outperforming some of the newly introduced state-of-the-art deep learning and hybrid models. This integrated approach to feature fusion successfully represents global signal patterns with localized transient features, providing a robust, repeatable approach to the analysis of industrial machine condition.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Erratum: Reductive Bias with Gated Recurrent Sentiment Analysis for Social Media Texts Using the Twitter Dataset]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0070</link>
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            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Multi-Horizon Feature Reconstruction for 3D Salient Object Detection]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0065</link>
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            <pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The recent improvements of current RGB-D salient object detection models have achieved better results by leveraging the depth modality in a convolutional neural network. Most existing approaches use a multi-stream architecture with enhanced feature fusion to subsequently determine saliency. The inconsistency of salient regions may result from the limitations of the acquisition devices and the low-depth images. Multi-horizon feature characteristics and complementary modalities are essential for resolving these problems. Even in cluttered backgrounds, complementary and multi-horizon features across several horizons must be taken into consideration since they provide structural and contextual information about the salient object. To maximize important features in low-depth images, the proposed model addresses these constraints. The multi-horizon features are optimized from the RGB and depth streams by using the proposed backbone network with the feature aggregation and optimization (FAO) module. The FAO module, which optimizes feature extraction by utilizing complementary features from the depth stream, is used to enhance the RGB stream. The proposed model investigates multi-horizon features between multi-resolution and multi-stage complementary features using a multi-horizon reconstruction module. To forecast optimal saliency, the FAO module combines data at multiple levels. A guiding framework for maximizing salient regions and reducing non-salient ones throughout the fusion process is provided by attention maps at various phases. Current evaluation metrics from the six publicly accessible difficult RGB-D datasets are used in the experimental investigation. Furthermore, we compare our findings with those of 18 other cutting-edge methods that have demonstrated encouraging performance on the above challenges.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Neuromorphic Computing for AI: Bridging Artificial and Biological Neural Networks Through Spiking Models]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0068</link>
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            <pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Neuromorphic computing is an emerging transformative approach inspired by the structure and functioning of biological neural networks toward enhancing the efficiency and adaptability of artificial intelligence systems. This work explores the use of spiking neural networks (SNNs), memristive devices and novel two-dimensional materials to advance neuromorphic computing technologies. We analyzed these systems using comparative experimental evaluation and discussed applications such as remote sensing scene classification and sensory processing based on recent neuromorphic computing studies. Results indicated reduced inference latency characteristics for SNN-based neuromorphic systems compared with conventional neural architectures under equivalent evaluation settings. The biomimetic neuromorphic sensory system based on electrolyte-gated transistors demonstrated approximately 15% lower estimated energy consumption compared with conventional processing approaches reported in related studies. Memristive neuromorphic devices demonstrated improved synaptic adaptation characteristics, indicating enhanced learning and plasticity behavior in comparison with conventional implementations reported in related studies. The study further discusses future prospects of neuromorphic computing using advanced materials and adaptive architectures.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Reintegration Barriers and Prospects on Emotional and Psychological Well-Being of Gulf Returnees in Kerala]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0069</link>
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            <pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This study examines the reintegration barriers and prospects affecting the emotional and psychological well-being of Gulf returnees in Kerala. The research analyses the difficulties encountered by return migrants across five key dimensions: financial constraints, limited government support (GS), family reintegration challenges, societal reintegration barriers, and career transition difficulties. Primary data were collected from 768 return migrants across selected districts in Kerala identified through the Kerala Migration Survey (2018). Using Structural Equation Modeling, the study identifies the most significant factors affecting the reintegration process. The findings reveal that economic difficulties—particularly debt, declining income, and reduced savings—and societal reintegration challenges represent the strongest barriers. Societal barriers mainly include difficulty rebuilding social networks, perceived social discrimination, and limited participation in local community organizations. Government program access also emerged as a major concern due to lack of awareness and bureaucratic constraints. In contrast, family reintegration and career transition factors showed comparatively weaker influence on overall reintegration challenges. The demographic analysis indicates that most returnees are middle-aged Muslim men who worked in low- to medium-skilled occupations in Gulf countries for more than a decade. The study highlights how economic insecurity and social marginalization indirectly affect the emotional and psychological well-being of return migrants. These findings underscore the need for targeted policy interventions, including improved financial assistance, accessible GS programs, and community-based reintegration initiatives to facilitate sustainable socio-economic and psychological adjustment for Gulf returnees in Kerala.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Blockchain-Enabled Secure Data Sharing Framework for Healthcare IoT Devices]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0071</link>
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            <pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The growing use of IoT devices in healthcare has increased medical data security challenges, as traditional access control models like role-based access control (RBAC), attribute-based access control (ABAC), and mandatory access control (MAC) lack flexibility and context awareness. The healthcare Internet of Things (IoT) faces challenges such as limited scalability, lack of dynamic access controls, and security risks from a centralized point of vulnerability. The research aims to develop a resilient, decentralized access control solution that provides secure, time-sensitive permissions in healthcare IoT systems. A hybrid RBAC-ABAC model, built on blockchain and integrated with interplanetary file system (IPFS), enables secure data sharing across healthcare IoT devices, outperforming current models in accuracy, latency, and scalability. A dual smart contract–IPFS mechanism enables adaptive access control. Results show 96.5% precision, 3.2 ms policy evaluation, 120 ms response time, 74 TPS, low cost (US$2.1), and 45–52 MB memory use. It outperforms ABAC, RBAC, and MAC, offering scalable, efficient security for healthcare IoT data sharing.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Quantum U-Net with Uncertainty Quantification for Brain Tumor Segmentation: A Hybrid Quantum-Classical Approach]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0063</link>
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            <pubDate>Thu, 03 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Precise segmentation of brain tumors from multi-modal magnetic resonance imaging (MRI) scans remains a challenging requirement for clinical diagnosis and treatment planning. This paper introduces a hybrid quantum-classical architecture that places a six-qubit variational quantum circuit (VQC) inside a 3D U-Net encoder-decoder. Rather than replacing the entire bottleneck, the VQC operates as a squeeze-and-excitation style channel attention block: global-average-pooled channel descriptors are angle-encoded into qubits, passed through two Strongly Entangling Layers, and the resulting measurements are applied as multiplicative channel weights. This noisy intermediate-scale quantum (NISQ)-compatible design uses only two layers and 36 parameters, so the quantum overhead per forward pass is confined to a single channel vector. For uncertainty estimation, we combine Monte Carlo Dropout entropy with quantum measurement variance to produce per-voxel confidence maps. Evaluated on Brain Tumor Segmentation 2020 with nine model variants, with test-time augmentation and adaptive thresholding, the proposed architecture reaches a mean Dice of 0.7812, HD95 of 23.87 (necrotic/non-enhancing core), 2.46 (ED), and 10.47 (ET), and an expected calibration error (ECE) of 0.0655—matching the strongest classical baseline on Dice while cutting ECE by roughly 20%. All stable quantum variants outperform their classical counterparts in calibration, confirming that quantum measurement stochasticity provides an additional, complementary uncertainty signal beyond dropout alone.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Novel Deep Learning Framework for Melanoma Diagnosis Using Enhanced Super Resolution Generative Adversarial Network and Convolutional Neural Network]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0066</link>
            <guid isPermaLink="false">/article/10.2478/ijssis-2026-0066</guid>
            <pubDate>Wed, 02 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Melanoma, a more aggressive class of skin cancer, develops from melanocytes, pigment-producing cells in the skin. Late diagnosis increases the risk of cancer spreading to other parts of the body, thereby decreasing the patient’s survival rate. An early diagnosis of melanoma cancer not only increases the survival rate but also improves the disease prognosis. Reduced medical costs incurred during cancer treatment are one of the major advantages of early cancer diagnosis. This research study proposes and implements a novel framework that leverages the enhanced super resolution generative adversarial network (ESRGAN) for pre-processing. The segmentation of the region of interest in preprocessed high-resolution images is carried out by density-based clustering algorithm with the Salp optimization technique. In the segmentation phase, the Salp Optimization algorithm computes the optimum cluster center for enhancing the performance of the density-based clustering algorithm. The classifier adopted in this study is a custom-built convolutional neural network model. The proposed convolutional neural network model with set parameters demonstrates a high accuracy and recall rate, deferring the need to adopt time-consuming and complex deep learning models. The experimental study evaluates the performance of the proposed methodology and emphasizes that the implemented diagnosis tool shows accuracy of 94.5% in melanoma diagnosis. The peak signal noise ratio (PSNR) and structural similarity index metrics are evaluated for the ESRGAN-generated images, showing significant increase in image resolution with the method adopted.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Neuromorphic Computing: Architectures, Learning Mechanisms, Hardware Platforms and Emerging Applications]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0061</link>
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            <pubDate>Wed, 02 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Neuromorphic computing is an emerging brain-inspired paradigm that enables efficient information processing by integrating computation and memory in massively parallel, event-driven architectures. Unlike traditional von Neumann systems, it is capable of alleviating—though not necessarily eliminating—constraints related to memory, power inefficiency and real-time learning latency, particularly in applications that are sparse, event-driven and time-sensitive designed for spike-based or in-memory computing; however, in applications that are dense, are throughput-oriented and involve batch processing, traditional computing systems may be more efficient. This review highlights key neuromorphic models, including spiking neural networks and biologically inspired learning rules such as spike-timing-dependent plasticity, which support adaptive and energy-efficient computation. It also discusses the evolution of neuromorphic hardware from digital platforms to analog and mixed-signal implementations using emerging technologies such as memristors and phase-change memory. Major applications and key challenges in neuromorphic computing are briefly outlined.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Validation of 5G-Enabled Mobile Gait Assessment System in a Clinical Setting]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0058</link>
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            <pubDate>Mon, 31 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents a non-wearable, vision-based human gait analysis system that enables accurate spatiotemporal gait assessment using a standard smartphone camera. The system employs a pretrained BlazePose model to extract 3D skeletal joint trajectories, from which clinically relevant gait parameters, including step length, gait speed and cadence, are derived. Unlike conventional gait analysis approaches that rely on expensive motion capture (MoCap) systems or wearable inertial sensors, the proposed framework offers a cost-effective and easily deployable alternative, eliminating the need for body-mounted devices. The system was validated on a cohort of 56 subjects, comprising 30 participants evaluated against a gold-standard MoCap system and 26 elderly patients assessed in a hospital setting. The results indicate that the proposed system achieves reliable gait measurement accuracy while maintaining practical feasibility for real-world clinical environments. This study demonstrates the potential of combining markerless computer vision with 5G connectivity to support objective gait assessment for remote monitoring.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Detection and Mitigation of Fruit Leaf Plant Diseases Using IoT with Machine Learning Techniques]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0051</link>
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            <pubDate>Sun, 30 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Healthy crops can be effectively grown through effective proactive practices in monitoring and managing plant diseases. Good plans can slow the spread of plant pathogens and even increase crop production. Moribund leaves on the fruits have a negative impact on crop integrity, thus undermining product quality. Carrying out early warnings of ills is in itself a difficult task. Plant pathogens are found to be highly diverse and intricate. The aim of the present work is the creation of an integrated system based on the combination of Internet-of-Things (IoT) sensors and machine learning methods to identify and prevent fruit leaf diseases. The research observes the symptoms, wilting and chlorosis, which may be used to determine the exact disease that is afflicting an Avocado plant. Image preprocessing can enhance the accuracy of the diagnosis by enhancing the quality of raw image data to emphasize the features that correspond with the disease and to reduce noise level; this is achieved with image preprocessing algorithms such as region-specific Adaptive Filters. The Hybrid Spectral “Thermal Sensor Fusion with Adaptive Thresholding” represents a new approach to the synthesis of thermal and spectral data. This leads to the earlier identification and treatment of diseases. The combination of Hybrid Vision Transformers and Convoluted Neural Networks also enhances the diagnosis of the avocado disease in order to detect those salient nuances and give a more global picture of the situation to quickly intervene. The study indicates that a combination of AI, machine learning, and remote sensing can provide more accurate and faster solutions to detect and control plant pathogens.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Lightweight Cryptography Algorithm for Secure Data Transmission in IoT Applications]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0064</link>
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            <pubDate>Sun, 30 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The rapid expansion of Internet of Things (IoT) networks introduces critical security challenges, particularly during data transmission. This manuscript presents a novel lightweight cryptography framework that integrates the Firefly Optimization with Elliptic Curve Cryptography (FA-ECC) to optimize key management and enhance data security. Additionally, a dense recurrent neural network (DRNN) module predicts and blocks malicious data entries, ensuring only legitimate data reaches the cloud. The proposed framework simultaneously improves security, efficiency and quality of service in IoT networks. Experimental evaluations demonstrate that the FA-ECC and DRNN integration effectively reduces energy consumption, transmission delay and vulnerability to attacks, establishing a secure and optimized data exchange mechanism for IoT environments.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Enhancing Cybersecurity Against Phishing Attacks Using VGA-Hyphishnet: A Hybrid Generative–Discriminative Model]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0062</link>
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            <pubDate>Tue, 25 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Traditional phishing detection systems based on machine learning (ML) and deep learning (DL) techniques largely rely on static patterns and manually engineered features. However, as phishing attacks continue to evolve in complexity and sophistication, these conventional approaches face significant limitations in accurately identifying emerging threats. To address this challenge, this study proposes VGA-HyPhishNet, a self-evolving generative artificial intelligence (AI) framework designed for real-time phishing URL detection. The proposed architecture integrates a generative encoder, variational generative modeling (VGM), a self-evolving generative threat engine (SE-GTE), and a hybrid generative–discriminative classifier to learn distribution-aware latent representations directly from raw URL strings. Unlike traditional discriminative models that rely solely on labeled patterns, the proposed framework leverages synthetic latent threat generation to dynamically adapt to evolving phishing behaviors and probabilistic structural patterns. The effectiveness of the proposed model was evaluated using the URL-Phish dataset, consisting of 111,660 URLs. Experimental results demonstrate that VGA-HyPhishNet achieves an accuracy of 98.73%, precision of 93.00%, recall of 94.97%, and an F1-score of 93.97%. Furthermore, comparative analysis with baseline models, including URL-transformer, BiLSTM-URL, and CharCNN-URL, shows that the proposed framework significantly reduces false positives (FP) and false negatives (FN) while maintaining low inference latency suitable for real-time deployment. Overall, the results indicate that the proposed work provides a robust and reliable solution for modern cybersecurity systems by leveraging generative representation learning and adaptive threat modeling, thereby improving resilience against zero-day phishing attacks and adversarial threats.]]></description>
            <category>ARTICLE</category>
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            <title><![CDATA[A Systematic Review of IoT and AI Technologies in Indian Agricultural Practices]]></title>
            <link>https://reference-global.com/article/10.2478/ijssis-2026-0060</link>
            <guid isPermaLink="false">/article/10.2478/ijssis-2026-0060</guid>
            <pubDate>Fri, 14 Aug 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Sustainable agriculture in India is facing numerous challenges, including an increase in atmospheric temperature, resource depletion, and higher productivity to feed a growing population. The amalgamation of Artificial Intelligence (AI) and the Internet of Things (IoT) offers revolutionary solutions for addressing these challenges and promoting sustainable farming practices. This promise of AI and IoT technologies enhancing agricultural efficiency, reducing waste, and optimizing resource management in India is discussed. Indian farmers can utilize the strength of AI to know current information on soil health, weather patterns, irrigation needs, and pest control. The paper also discusses the barriers to adoption, including technological infrastructure, affordability, and the need for digital literacy among farmers. Furthermore, it highlights the integrated application of AI and IoT technologies, along with comprehensive results and a gap analysis, as a strategic approach to advancing sustainable agriculture and ensuring food security in India. The findings underscore the potential of these technologies to enhance environmental stewardship and strengthen economic resilience within the agricultural sector.]]></description>
            <category>ARTICLE</category>
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