Published Online: 31 December 2025.

J. Smart Sens. Comput., 2025, 1(3), 25216 | Volume 1 Issue 3 (December 2025) | DOI: https://doi.org/10.64189/ssc.25216

This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)

Editorial Comments: Journal of Smart Sensors and

Computing, Volume 1 Issue 3

Thittaporn Ganokratanaa

1,2,*

Department of Mathematics, Faculty of Science, King Mongkut's University of Technology, Bangkok, 10140, Thailand

Editor-in-Chief, Journal of Smart Sensors and Computing, GR Scholastic, Ahmedabad, Gujarat, 382424, India

*Email: eic.ssc@gr-journals.com (T. Ganokratanaa)

This issue of the Journal of Smart Sensors & Computing (Volume 1, Issue 3, December 2025) features a

multidisciplinary collection of research articles covering smart agriculture, neuromorphic computing, AI-based disease

detection and prediction, and machine learning applications in loan default prediction. The issue comprises three

original research articles and one review article, reflecting the journal’s emphasis on methodological rigor, applied

relevance, and interdisciplinary integration across smart sensing and computational intelligence domains.

Salve et al.

[1]

present a comprehensive study on smart agriculture systems that integrate the Internet of Things (IoT)

and Machine Learning (ML) to enhance crop monitoring, optimize resource utilization, and support sustainable

farming practices. IoT-based wireless sensor networks (WSNs) enable continuous real-time acquisition of

environmental and soil parameters, while ML algorithms analyze the collected data to facilitate informed decision-

making. Experimental results demonstrate that the proposed ensemble-based ML model achieves high predictive

accuracy, validating the effectiveness of combining multiple learning algorithms for smart agriculture applications.

Jadhav et al.

[2]

introduce UNAL (Unified Adaptive, Hardware-Agnostic Neuromorphic Assembly Layer), a novel

compilation framework that translates high-level Spiking Neural Network (SNN) models into portable, spike-level

assembly across heterogeneous neuromorphic platforms. The framework incorporates a unified intermediate

representation (UNAL-IR), a compact instruction set, and an optimization-driven mapping pipeline that jointly

addresses latency, energy efficiency, routing congestion, and adaptability. Quantitative evaluations on standard SNN

benchmarks (DVS Gesture and CIFAR-10 SNN) mapped to Intel Loihi 2 demonstrate 18–32% latency reduction, 21–

38% energy savings, and 25–40% lower routing congestion compared to Loihi-native and platform-specific toolchains.

A smart-city surveillance case study further validates the framework’s capability for real-time edge deployment,

establishing UNAL as a scalable and future-ready neuromorphic compiler infrastructure. Shegar et al.

[3]

propose a

multi-class skin lesion classification framework based on transfer learning, integrating an EfficientNet-B3 backbone

with a Convolutional Block Attention Module (CBAM) to enhance discriminative feature learning. EfficientNet-B3,

pre-trained on large-scale natural image datasets, serves as a robust feature extractor, while CBAM adaptively

emphasizes informative channel and spatial features, enabling the network to focus on diagnostically relevant lesion

regions while suppressing background artifacts. The model is trained and evaluated on the DermNet-23 dataset

comprising 23 clinically significant skin disease classes. Experimental results show that the proposed EfficientNet-B3

+ CBAM model achieves 87.1% accuracy, an 85.6% macro-F1 score, and a 0.94 AUC, outperforming baseline CNN,

ResNet50, MobileNetV3, and standard EfficientNet-B3 models. Gour et al.

[4]

investigated the performance of

ensemble machine learning algorithms, including Random Forest, Gradient Boosting, XGBoost, and LightGBM, for

loan default prediction. Using a publicly available benchmark dataset, the study adopts a systematic experimental

workflow involving data preprocessing, feature engineering, class imbalance handling, model training, and

performance evaluation. The results, assessed using standard metrics such as accuracy, precision, recall, F1-score, and

ROC-AUC, demonstrate the effectiveness of ensemble learning approaches in improving predictive performance for

financial risk assessment.

Use of artificial intelligence (AI)-assisted technology for manuscript preparation

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing

or editing of the manuscript and no images were manipulated using AI.

References

[1] S. Salve, P. Dhotre, K. Sathe, V. Salegaye, Internet of things and machine learning in smart agriculture: A

comprehensive review, Journal of Smart Sensors and Computing, 2025, 1(3), 25214, doi: 10.64189/ssc.25214.

[2] G. D. Jadhav, R. V. Dagade, S. Jakhade, K. Jadhav, R. Hinge, S. Joshi, The unified neuromorphic assembly layer

for hardware-agnostic compilation in neuromorphic computing, Journal of Smart Sensors and Computing, 2025, 1(3),

25212, doi: 10.64189/ssc.25212.

[3] S. R. Shegar, S. S. Patil, Multi-class skin lesion classification using transfer learning with EfficientNet-B3 and

convolutional block attention module, Journal of Smart Sensors and Computing, 2025, 1(3), 25213, doi:

10.64189/ssc.25213.

[4] P. Soni, S. Gour, Loan default prediction using ensemble machine learning algorithms, Journal of Smart Sensors

and Computing, 2025, 1(3), 25215, doi: 10.64189/ssc.25215.

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