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Menampilkan 1–10 dari 27 artikel
Enhancing Sustainability in Linear WSNs Using DSDVTRI: A Triple-Interleaving Routing Approach for Pipeline Monitoring
Kumaran, Divya Nacciar
; Siva Kumar Subramaniam
; Ahamed Fayeez Bin Tuani Ibrahim
; Vigneswara Rao Gannapathy
The safety and efficiency of oil and gas pipelines remain critical due to the high risks associated with leaks, pressure surges, and undetected structural damage. WSNs provide an effective solution for real-time monitoring by deploying sensor nodes along the pipeline. However, existing routing protocols such as DSDV and AODV face challenges with congestion, packet loss, and uneven energy consumption in long linear topologies. This study proposes DSDVTRI, a triple-interleaving extension of the DS...
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Deep Learning-Based Classification of Cognitive Workload Using Functional Connectivity Features
Vineeta Khemchandani
; Alok Singh Chauhan
; Shahnaz Fatima
; Jalauk Singh Maurya
; Abhay Singh Rathaur
; Kumar Sharma, Narendra
; Daya Shankar Srivastava
; Vugar Abdullayev
Advance Sustainable Science, Engineering and Technology (ASSET)
Vol 8
, No 1
(2026)
Cognitive workload plays a vital role in tasks that demand dynamic decision-making, especially under high-risk and time-sensitive conditions. An excessive workload can lead to unexpected and disproportionate risks, whereas insufficient workload may cause disengagement, undermining task performance. This underscores the importance of maintaining an optimal level of mental focus in high-pressure situations to ensure successful task execution. This study leverages deep learning methods alongside fu...
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Integrating Fully Homomorphic Encryption and Zero-Knowledge Proofs for Efficient Verifiable Computation
Journal of Computing Theories and Applications
Vol 3
, No 3
(2025)
Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information, although they do not support encrypted computation independently. This study integrates bo...
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1 Sitasi
Seasonal Variability in Soil Salinity and its Climatic Drivers in Khulna, Bangladesh
Forum Geografi
Vol 39
, No 3
(2025)
Bangladesh is one of the countries in the world most severely affected by soil salinity issues. This research focuses on the seasonal variation in soil salinity and the associated impact of climate change across different sites in the Batiaghata sub-district of Khulna, located in the southwestern coastal belt of Bangladesh. The study encompasses four meteorological seasons: pre-monsoon (March-April-May), monsoon (June-July-August-September), post-monsoon (October-November), and winter (December-...
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PENERAPAN METODE K-MEANS CLUSTERING UNTUK PENGELOMPOKAN SISWA BERDASARKAN PRESTASI DI SD N 03 SANGGANG SUKOHARJO
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN)
Vol 13
, No 2
(2025)
The rapid advancement of data science has made data processing a critical requirement across various fields, including education. Educational institutions are increasingly required to leverage available resources and information systems to enhance competitiveness and support strategic decision-making. Student achievement is generally assessed through both theoretical and practical subjects; however, determining achievement groups (very good, good, sufficient) often lacks efficiency, limiting ear...
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Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction
Kusuma, Muh Galuh Surya Putra
; Setiadi, De Rosal Ignatius Moses
; Herowati, Wise
; Sutojo, T.
; Adi, Prajanto Wahyu
; Dutta, Pushan Kumar
; Nguyen, Minh T.
Journal of Computing Theories and Applications
Vol 3
, No 2
(2025)
Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, dee...
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2 Sitasi
A Comparative Analysis of Time-Series Models of ARIMA and Prophet IoT-Based Flood Forecasting in Sungai Melaka
Mazran Esro
; Siva Kumar Subramaniam
; Tuani Ibrahim, Ahamed Fayeez
; Yogan Jaya Kumar
; Siti Aisyah Anas
; Sujatha Rajkumar
Advance Sustainable Science, Engineering and Technology (ASSET)
Vol 7
, No 4
(2025)
Flood prediction is essential for mitigating disasters, especially in low-lying areas. This study presents an IoT-driven flood forecasting system that utilizes ARIMA and Prophet models to predict water levels in Sungai Melaka, Malaysia. Sensor data collected from an IoT-based flood observatory system was used to train and evaluate both models. Performance analysis based on RMSE and MAPE revealed that while ARIMA captures short-term trends, Prophet outperforms it with a lower MAPE of 6% and RMSE...
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1 Sitasi
Enhanced Air Quality Prediction Using AI: A Comparative Study of GRU, CNN, and XGBoost Models
Kayam Saikumar
; Munugapati Bhavana
; Rayudu Prasanthi
; Singaraju Suguna Mallika
; Deepthi Kamidi
; Naveen Malik
; Kapil Joshi
Advance Sustainable Science, Engineering and Technology (ASSET)
Vol 7
, No 3
(2025)
Weather monitoring is vital due to environmental changes and rising air pollution, which affects health and lifestyles. Accurate air quality prediction models are essential yet challenging due to complex weather-pollution interactions. This study employs explainable deep learning and machine learning techniques—GRU, CNN, and XGBoost—on a custom dataset of 100,000 samples with 15 features, including PM2.5, PM10, humidity, and temperature. Using SHAP for interpretability, the GRU model outperforms...
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Personal branding dalam menciptakan identitas digital (studi kasus Irfan hamid pada akun TikTok @Darahdenbiru)
Sosio Dialektika : Jurnal Ilmu Sosial Humaniora
Vol 10
, No 1
(2025)
Abstrak
Penelitian ini bertujuan untuk mengeksplorasi bagaimana Irfan Hamid, seorang selebgram TikTok pemilik akun @darahdenbiru, merancang personal branding dan membentuk identitas digital melalui konten serta aktivitasnya di media sosial. Penelitian ini menggunakan pendekatan kualitatif deskriptif dengan metode studi kasus. Teknik pengumpulan data dilakukan melalui dokumentasi, observasi, dan wawancara langsung dengan subjek penelitian. Landasan teori yang digunakan mengacu pada delapan prinsi...
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IMPLEMENTASI METODE CERTAINTY FAKTOR DALAM SISTEM PAKAR UNTUK DIAGNOSA KERUSAKAN LAPTOP
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN)
Vol 13
, No 1
(2025)
Expert systems have been used in various fields. With the aim of helping users solve the problems they face. This study presents the application of the Certainty Factor (CF) method in an expert system designed to diagnose laptop damage based on symptoms experienced by users. This system aims to help technicians and general users identify potential laptop malfunctions with better accuracy. The expert system developed combines a comprehensive knowledge base consisting of various laptop components...
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