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		<title>NPS Australia Submission System</title>
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			<title>NPS Australia Submission System</title>
			<pubDate><![CDATA[Thu, 27 Aug 2026 11:09:48 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-decentralized-and-privacy-adaptive-ride-sharing-framework-using-blockchain-and-differential-privacy-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-decentralized-and-privacy-adaptive-ride-sharing-framework-using-blockchain-and-differential-privacy-2/]]></link>
			<title>A Decentralized and Privacy-Adaptive Ride-Sharing Framework Using Blockchain and Differential Privacy</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 21:37:01 +0000]]></pubDate>
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			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/deep-learning-based-ordinal-severity-grading-of-mustard-flea-beetle-infestation-from-plant-images-2/]]></link>
			<title>Deep Learning-Based Ordinal Severity Grading of Mustard Flea Beetle Infestation from Plant Images</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 20:38:36 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/an-explainable-transfer-learning-framework-based-on-efficientnetb3-for-multi-class-brain-tumor-classification-from-mri-images/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/an-explainable-transfer-learning-framework-based-on-efficientnetb3-for-multi-class-brain-tumor-classification-from-mri-images/]]></link>
			<title>An Explainable Transfer Learning Framework Based on EfficientNetB3 for Multi-Class Brain Tumor Classification from MRI Images</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 19:59:10 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/towards-edge-artificial-intelligence-for-predicting-antimicrobial-resistance-in-mycobacterium-tuberculosis-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/towards-edge-artificial-intelligence-for-predicting-antimicrobial-resistance-in-mycobacterium-tuberculosis-2/]]></link>
			<title>Towards Edge Artificial Intelligence for Predicting Antimicrobial Resistance in Mycobacterium tuberculosis</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 19:31:02 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/aris-an-iot-digital-twin-architecture-for-ai-assisted-adaptive-management-of-fragile-island-tourism-ecosystems/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/aris-an-iot-digital-twin-architecture-for-ai-assisted-adaptive-management-of-fragile-island-tourism-ecosystems/]]></link>
			<title>ARIS: An IoT–Digital Twin Architecture for AI-Assisted Adaptive Management of Fragile Island Tourism Ecosystems</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 18:59:23 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/an-integrated-edge-ai-and-iot-system-for-quality-control-waste-reduction-and-predictive-maintenance-in-small-scale-food-processing/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/an-integrated-edge-ai-and-iot-system-for-quality-control-waste-reduction-and-predictive-maintenance-in-small-scale-food-processing/]]></link>
			<title>An Integrated Edge AI and IoT System for Quality Control, Waste Reduction and Predictive Maintenance in Small-Scale Food Processing</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 18:22:58 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/agroconv-eff-a-hybrid-convnext-efficientnetv2-architecture-with-adaptive-feature-gating-for-multi-crop-plant-disease-detection-in-bangladeshi-agriculture/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/agroconv-eff-a-hybrid-convnext-efficientnetv2-architecture-with-adaptive-feature-gating-for-multi-crop-plant-disease-detection-in-bangladeshi-agriculture/]]></link>
			<title>AgroConv-Eff: A Hybrid ConvNeXt–EfficientNetV2 Architecture with Adaptive Feature Gating for Multi-Crop Plant Disease Detection</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 18:05:18 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/unsupervised-eeg-based-subgroup-discovery-in-alzheimers-disease-using-spectral-and-temporal-features/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/unsupervised-eeg-based-subgroup-discovery-in-alzheimers-disease-using-spectral-and-temporal-features/]]></link>
			<title>Unsupervised EEG-Based Subgroup Discovery in Alzheimer’s Disease Using Spectral and Temporal Features</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 17:58:55 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/quantum-resilient-self-healing-cybersecurity-architecture-for-iot-enabled-smart-grids-using-federated-intelligence-and-digital-twins/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/quantum-resilient-self-healing-cybersecurity-architecture-for-iot-enabled-smart-grids-using-federated-intelligence-and-digital-twins/]]></link>
			<title>Quantum-Resilient Self-Healing Cybersecurity Architecture for IoT-Enabled Smart Grids Using Federated Intelligence and Digital Twins</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 17:50:49 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/unsupervised-eeg-based-subgroup-discovery-in-alzheimers-disease-using-spectral-and-temporal-features-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/unsupervised-eeg-based-subgroup-discovery-in-alzheimers-disease-using-spectral-and-temporal-features-2/]]></link>
			<title>Unsupervised EEG-Based Subgroup Discovery in Alzheimer&#8217;s Disease Using Spectral and Temporal Features</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 17:50:44 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/an-empirical-study-on-the-impact-of-authorship-on-the-bug-proneness-of-code-clones/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/an-empirical-study-on-the-impact-of-authorship-on-the-bug-proneness-of-code-clones/]]></link>
			<title>An Empirical Study on the Impact of Authorship on the Bug-proneness of Code Clones</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 17:47:34 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/do-climate-shocks-affect-rice-yield-in-bangladesh-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/do-climate-shocks-affect-rice-yield-in-bangladesh-2/]]></link>
			<title>Do Climate Shocks Affect Rice Yield in Bangladesh?</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 17:40:21 +0000]]></pubDate>
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					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/ugpda-and-bdai-robust-medical-semi-supervised-learning-under-varied-labeled-ratios-and-class-imbalance-profiles/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/ugpda-and-bdai-robust-medical-semi-supervised-learning-under-varied-labeled-ratios-and-class-imbalance-profiles/]]></link>
			<title>UGPDA and BDAI: Robust Medical Semi-Supervised Learning Under Varied Labeled Ratios and Class-Imbalance Profiles</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 17:33:14 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/optimal-ramp-rate-control-using-battery-energy-storage-systems-bess-for-utility-scale-solar-power-plants-in-bangladesh/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/optimal-ramp-rate-control-using-battery-energy-storage-systems-bess-for-utility-scale-solar-power-plants-in-bangladesh/]]></link>
			<title>Optimal Ramp-Rate Control Using Battery Energy Storage Systems (BESS) for Utility-Scale Solar Power Plants in Bangladesh</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 16:33:20 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-comprehensive-benchmark-of-recurrent-convolutional-and-attention-based-models-for-radar-emitter-classification-under-photon-starved-conditions-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-comprehensive-benchmark-of-recurrent-convolutional-and-attention-based-models-for-radar-emitter-classification-under-photon-starved-conditions-2/]]></link>
			<title>A Comprehensive Benchmark of Recurrent, Convolutional, and Attention-Based Models for Radar Emitter Classification Under Photon-Starved Conditions</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 15:50:43 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/forecasting-independent-evolution-possibilities-of-code-clones-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/forecasting-independent-evolution-possibilities-of-code-clones-2/]]></link>
			<title>Forecasting Independent Evolution Possibilities of Code Clones</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 15:29:46 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/provable-moving-target-defense-for-cyber-physical-networks-from-the-security-performance-frontier-to-no-regret-defense/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/provable-moving-target-defense-for-cyber-physical-networks-from-the-security-performance-frontier-to-no-regret-defense/]]></link>
			<title>Provable Moving-Target Defense for Cyber-Physical Networks: From the Security–Performance Frontier to No-Regret Defense</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 15:21:12 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/reputation-over-geometry-a-validation-guided-robust-federated-defense-for-imbalanced-drone-intrusion-detection/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/reputation-over-geometry-a-validation-guided-robust-federated-defense-for-imbalanced-drone-intrusion-detection/]]></link>
			<title>Reputation over Geometry: A Validation-Guided Robust Federated Defense for Imbalanced Drone Intrusion Detection</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 15:17:34 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/bagc-fl-benign-anchor-gradient-correction-for-imbalance-aware-federated-intrusion-detection-in-iomt-networks/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/bagc-fl-benign-anchor-gradient-correction-for-imbalance-aware-federated-intrusion-detection-in-iomt-networks/]]></link>
			<title>BAGC-FL: Benign-Anchor Gradient Correction for Imbalance-Aware Federated Intrusion Detection in IoMT Networks</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 14:54:08 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/lightweight-and-explainable-ai-driven-intrusion-detection-for-iot-networks-a-resource-efficient-machine-learning-framework-with-shap-based-interpretation/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/lightweight-and-explainable-ai-driven-intrusion-detection-for-iot-networks-a-resource-efficient-machine-learning-framework-with-shap-based-interpretation/]]></link>
			<title>Lightweight and Explainable AI-Driven Intrusion Detection for IoT Networks: A Resource-Efficient Machine Learning Framework with SHAP-Based Interpretation</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 14:36:52 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/ai-enhanced-digital-twin-with-hardware-in-the-loop-deployment-for-real-time-lithium-ion-battery-state-of-health-estimation/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/ai-enhanced-digital-twin-with-hardware-in-the-loop-deployment-for-real-time-lithium-ion-battery-state-of-health-estimation/]]></link>
			<title>AI-Enhanced Digital Twin with Hardware-in-the-Loop Deployment for Real-Time Lithium-Ion Battery State-of-Health Estimation</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 14:31:10 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/explainyolov11-a-robust-and-interpretable-yolov11-framework-for-real-time-traffic-sign-detection-in-autonomous-vehicles-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/explainyolov11-a-robust-and-interpretable-yolov11-framework-for-real-time-traffic-sign-detection-in-autonomous-vehicles-2/]]></link>
			<title>ExplainYOLOv11: A Robust and Interpretable YOLOv11 Framework for Real-Time Traffic Sign Detection in Autonomous Vehicles</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 14:12:14 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-hybrid-modified-des-keccak-cryptographic-framework-with-dynamic-s-box-design-for-secure-data-confidentiality-and-integrity-verification-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-hybrid-modified-des-keccak-cryptographic-framework-with-dynamic-s-box-design-for-secure-data-confidentiality-and-integrity-verification-2/]]></link>
			<title>A Hybrid Modified DES–Keccak Cryptographic Framework with Dynamic S-Box Design for Secure Data Confidentiality and Integrity Verification</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 14:03:56 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/pulse-elf-temporal-channel-learning-and-low-overhead-feedback-for-integrated-sensing-and-communication-in-commodity-wi-fi-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/pulse-elf-temporal-channel-learning-and-low-overhead-feedback-for-integrated-sensing-and-communication-in-commodity-wi-fi-2/]]></link>
			<title>PULSE-ELF: Temporal Channel Learning and Low-Overhead Feedback for Integrated Sensing and Communication in Commodity Wi-Fi</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 13:57:45 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/analysis-of-various-machine-learning-model-performances-for-epilepsy-prediction/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/analysis-of-various-machine-learning-model-performances-for-epilepsy-prediction/]]></link>
			<title>Analysis of Various Machine Learning Model  Performances for Epilepsy Prediction</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 12:56:01 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/paddy-seeds-classification-using-machine-learning-models-for-public-health/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/paddy-seeds-classification-using-machine-learning-models-for-public-health/]]></link>
			<title>Paddy Seeds Classification using Machine Learning  Models for Public Health</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 12:54:10 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/machine-learning-applications-in-the-classification-of-dates/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/machine-learning-applications-in-the-classification-of-dates/]]></link>
			<title>Machine Learning Applications in the Classification  of Dates</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 12:52:25 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/elevating-skin-cancer-diagnosis-a-study-of-various-machine-learning-approaches/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/elevating-skin-cancer-diagnosis-a-study-of-various-machine-learning-approaches/]]></link>
			<title>Elevating Skin Cancer Diagnosis: A Study of Various  Machine Learning Approaches</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 12:50:25 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-comparative-deep-learning-approach-cnn-and-hybrid-algorithms-for-lung-cancer-classification-in-histopathology-images/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-comparative-deep-learning-approach-cnn-and-hybrid-algorithms-for-lung-cancer-classification-in-histopathology-images/]]></link>
			<title>A Comparative Deep Learning Approach: CNN And  Hybrid Algorithms For Lung Cancer Classification In  Histopathology Images</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 12:47:01 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-closed-loop-supply-chain-model-for-end-of-life-electronics-under-carbon-tax-policy/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/a-closed-loop-supply-chain-model-for-end-of-life-electronics-under-carbon-tax-policy/]]></link>
			<title>A Closed-Loop Supply Chain Model for End-of-Life Electronics under Carbon Tax Policy</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 11:10:24 +0000]]></pubDate>
		</item>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/interpretable-short-term-electricity-demand-forecasting-for-the-pgcb-grid-using-feature-fusion/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/interpretable-short-term-electricity-demand-forecasting-for-the-pgcb-grid-using-feature-fusion/]]></link>
			<title>Interpretable Short-Term Electricity Demand Forecasting for the PGCB Grid Using Feature Fusion</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 10:03:05 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/30-day-ahead-load-forecasting-for-the-rajshahi-zone-of-the-bangladesh-power-system-a-linear-base-boosted-bilstm-approach/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/30-day-ahead-load-forecasting-for-the-rajshahi-zone-of-the-bangladesh-power-system-a-linear-base-boosted-bilstm-approach/]]></link>
			<title>30-Day-Ahead Load Forecasting for the Rajshahi Zone of the Bangladesh Power System: A Linear-Base Boosted BiLSTM Approach</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 09:55:22 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/endostack-xai-a-hybrid-and-explainable-machine-learning-framework-for-endometriosis-prediction/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/endostack-xai-a-hybrid-and-explainable-machine-learning-framework-for-endometriosis-prediction/]]></link>
			<title>EndoStack-XAI: A Hybrid and Explainable Machine Learning Framework for Endometriosis Prediction</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 08:45:04 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/addressing-extensive-missingness-in-newspaper-reported-suicide-data-through-imputation-and-clustering-techniques/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/addressing-extensive-missingness-in-newspaper-reported-suicide-data-through-imputation-and-clustering-techniques/]]></link>
			<title>Addressing Extensive Missingness in Newspaper-Reported Suicide Data Through Imputation and Clustering Techniques</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 08:00:03 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/toward-privacy-preserving-federated-brain-tumor-mri-classification-a-hybrid-cnn-transformer-framework-and-preliminary-evaluation/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/toward-privacy-preserving-federated-brain-tumor-mri-classification-a-hybrid-cnn-transformer-framework-and-preliminary-evaluation/]]></link>
			<title>Toward Privacy-Preserving Federated Brain Tumor MRI Classification: A Hybrid CNN &#8211; Transformer Framework and Preliminary Evaluation</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 07:52:23 +0000]]></pubDate>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/physics-and-communication-aware-remote-terminal-unit-cyberattacks-detection-and-classification-for-smart-energy-grids/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/physics-and-communication-aware-remote-terminal-unit-cyberattacks-detection-and-classification-for-smart-energy-grids/]]></link>
			<title>Physics and Communication-Aware Remote Terminal Unit Cyberattacks Detection and Classification for Smart Energy Grids</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 07:42:56 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/explainable-ai-based-thyroid-diagnosis-classification-using-shap-guided-feature-reduction/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/explainable-ai-based-thyroid-diagnosis-classification-using-shap-guided-feature-reduction/]]></link>
			<title>Explainable AI-Based Thyroid Diagnosis Classification Using SHAP-Guided Feature Reduction</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 06:12:42 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/ai-enhanced-smart-grid-integration-for-solar-energy-a-comprehensive-bibliometric-analysis/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/ai-enhanced-smart-grid-integration-for-solar-energy-a-comprehensive-bibliometric-analysis/]]></link>
			<title>AI-Enhanced Smart Grid Integration for Solar  Energy: A Comprehensive Bibliometric Analysis</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 04:53:20 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/comparative-techno-economic-analysis-of-si-gaas-and-ingan-thin-film-solar-cells-under-bangladeshs-climatic-conditions/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/comparative-techno-economic-analysis-of-si-gaas-and-ingan-thin-film-solar-cells-under-bangladeshs-climatic-conditions/]]></link>
			<title>Comparative Techno-Economic Analysis of Si, GaAs, and InGaN Thin-Film Solar Cells under Bangladesh’s Climatic Conditions</title>
			<pubDate><![CDATA[Sun, 30 Aug 2026 03:29:33 +0000]]></pubDate>
		</item>
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			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/exploring-possible-adoption-factors-of-mhealth-applications-among-women-in-bangladesh/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/exploring-possible-adoption-factors-of-mhealth-applications-among-women-in-bangladesh/]]></link>
			<title>EXPLORING POSSIBLE ADOPTION FACTORS OF mHealth APPLICATIONS AMONG WOMEN IN BANGLADESH</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 20:36:37 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/evaluating-synthetic-oversampling-strategies-for-imbalanced-thyroid-disease-classification-with-shap-based-explainability-a-comparative-study-using-ensemble-classifiers/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/evaluating-synthetic-oversampling-strategies-for-imbalanced-thyroid-disease-classification-with-shap-based-explainability-a-comparative-study-using-ensemble-classifiers/]]></link>
			<title>Evaluating Synthetic Oversampling Strategies for Imbalanced Thyroid Disease Classification with SHAP-Based Explainability: A Comparative Study Using Ensemble Classifiers.</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 20:07:26 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/optimization-of-operational-parameters-of-incline-plate-planter-via-response-surface-methodology/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/optimization-of-operational-parameters-of-incline-plate-planter-via-response-surface-methodology/]]></link>
			<title>Optimization of operational parameters of incline plate planter via Response surface methodology</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 16:51:29 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/comparative-analysis-and-shunt-reactor-mitigation-of-the-ferranti-effect-in-132-kv-overhead-and-underground-transmission-lines-2/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/comparative-analysis-and-shunt-reactor-mitigation-of-the-ferranti-effect-in-132-kv-overhead-and-underground-transmission-lines-2/]]></link>
			<title>Comparative Analysis and Shunt-Reactor Mitigation of the Ferranti Effect in 132-kV Overhead and Underground Transmission Lines</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 15:52:49 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/omnicortex-a-hybrid-framework-for-explainable-multimodal-brain-tumor-classification/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/omnicortex-a-hybrid-framework-for-explainable-multimodal-brain-tumor-classification/]]></link>
			<title>OmniCortex: A Hybrid Framework for Explainable Multimodal Brain Tumor Classification</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 15:51:05 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/automated-quality-control-in-textiles-a-computer-vision-blended-approach-to-fabric-defect-detection/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/automated-quality-control-in-textiles-a-computer-vision-blended-approach-to-fabric-defect-detection/]]></link>
			<title>Automated Quality Control in Textiles: A Computer Vision Blended Approach to Fabric Defect Detection</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 15:35:44 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/multi-class-mri-brain-tumor-classification-using-a-hybrid-efficientnet-attention-architecture/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/multi-class-mri-brain-tumor-classification-using-a-hybrid-efficientnet-attention-architecture/]]></link>
			<title>Multi-Class MRI Brain Tumor Classification Using a Hybrid EfficientNet-Attention Architecture</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 15:32:03 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/scopeguard-mitigating-goal-drift-and-confused-deputy-attacks-in-mcp-agent-pipelines/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/scopeguard-mitigating-goal-drift-and-confused-deputy-attacks-in-mcp-agent-pipelines/]]></link>
			<title>ScopeGuard: Mitigating Goal Drift and Confused Deputy Attacks in MCP Agent Pipelines</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 14:56:22 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/traffic-demand-regulation-by-an-intelligent-management-system-in-dhaka-city/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/traffic-demand-regulation-by-an-intelligent-management-system-in-dhaka-city/]]></link>
			<title>Traffic Demand Regulation by an Intelligent Management System in Dhaka City</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 14:02:04 +0000]]></pubDate>
		</item>
					<item>
			<guid><![CDATA[https://nps-ss.com.au/nps-paper-submissions/benchmarking-deep-learning-models-for-iomt-intrusion-detection-with-mcnemar-testing-and-explainable-artificial-intelligence/]]></guid>
			<link><![CDATA[https://nps-ss.com.au/nps-paper-submissions/benchmarking-deep-learning-models-for-iomt-intrusion-detection-with-mcnemar-testing-and-explainable-artificial-intelligence/]]></link>
			<title>Benchmarking Deep Learning Models for IoMT Intrusion Detection with McNemar Testing and  Explainable Artificial Intelligence</title>
			<pubDate><![CDATA[Sat, 29 Aug 2026 13:34:13 +0000]]></pubDate>
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